Relevant bibliographies by topics / Single cell sequencing data

Academic literature on the topic 'Single cell sequencing data'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Single cell sequencing data"

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Shi, Qianqian, Xinxing Li, Qirui Peng, Chuanchao Zhang, and Luonan Chen. "scDA: Single cell discriminant analysis for single-cell RNA sequencing data." Computational and Structural Biotechnology Journal 19 (2021): 3234–44. http://dx.doi.org/10.1016/j.csbj.2021.05.046.

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Zhao, Xinlei, Shuang Wu, Nan Fang, Xiao Sun, and Jue Fan. "Evaluation of single-cell classifiers for single-cell RNA sequencing data sets." Briefings in Bioinformatics 21, no. 5 (October 23, 2019): 1581–95. http://dx.doi.org/10.1093/bib/bbz096.

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Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.
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Satas, Gryte, and Benjamin J. Raphael. "Haplotype phasing in single-cell DNA-sequencing data." Bioinformatics 34, no. 13 (June 27, 2018): i211—i217. http://dx.doi.org/10.1093/bioinformatics/bty286.

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Vallejos, Catalina A., John C. Marioni, and Sylvia Richardson. "BASiCS: Bayesian Analysis of Single-Cell Sequencing Data." PLOS Computational Biology 11, no. 6 (June 24, 2015): e1004333. http://dx.doi.org/10.1371/journal.pcbi.1004333.

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Schnepp, Patricia M., Mengjie Chen, Evan T. Keller, and Xiang Zhou. "SNV identification from single-cell RNA sequencing data." Human Molecular Genetics 28, no. 21 (August 27, 2019): 3569–83. http://dx.doi.org/10.1093/hmg/ddz207.

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Abstract Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.
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Gisina, Alisa, Irina Kholodenko, Yan Kim, Maxim Abakumov, Alexey Lupatov, and Konstantin Yarygin. "Glioma Stem Cells: Novel Data Obtained by Single-Cell Sequencing." International Journal of Molecular Sciences 23, no. 22 (November 17, 2022): 14224. http://dx.doi.org/10.3390/ijms232214224.

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Glioma is the most common type of primary CNS tumor, composed of cells that resemble normal glial cells. Recent genetic studies have provided insight into the inter-tumoral heterogeneity of gliomas, resulting in the updated 2021 WHO classification of gliomas. Thorough understanding of inter-tumoral heterogeneity has already improved the prognosis and treatment outcomes of some types of gliomas. Currently, the challenge for researchers is to study the intratumoral cell heterogeneity of newly defined glioma subtypes. Cancer stem cells (CSCs) present in gliomas and many other tumors are an example of intratumoral heterogeneity of great importance. In this review, we discuss the modern concept of glioma stem cells and recent single-cell sequencing-driven progress in the research of intratumoral glioma cell heterogeneity. The particular emphasis was placed on the recently revealed variations of the cell composition of the subtypes of the adult-type diffuse gliomas, including astrocytoma, oligodendroglioma and glioblastoma. The novel data explain the inconsistencies in earlier glioma stem cell research and also provide insight into the development of more effective targeted therapy and the cell-based immunotherapy of gliomas. Separate sections are devoted to the description of single-cell sequencing approach and its role in the development of cell-based immunotherapies for glioma.
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Dai, Hao, Lin Li, Tao Zeng, and Luonan Chen. "Cell-specific network constructed by single-cell RNA sequencing data." Nucleic Acids Research 47, no. 11 (March 13, 2019): e62-e62. http://dx.doi.org/10.1093/nar/gkz172.

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Zhang, Yinan, Xiaowei Xie, Peng Wu, and Ping Zhu. "SIEVE: identifying robust single cell variable genes for single-cell RNA sequencing data." Blood Science 3, no. 2 (April 2021): 35–39. http://dx.doi.org/10.1097/bs9.0000000000000072.

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Myers, Matthew A., Simone Zaccaria, and Benjamin J. Raphael. "Identifying tumor clones in sparse single-cell mutation data." Bioinformatics 36, Supplement_1 (July 1, 2020): i186—i193. http://dx.doi.org/10.1093/bioinformatics/btaa449.

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Abstract Motivation Recent single-cell DNA sequencing technologies enable whole-genome sequencing of hundreds to thousands of individual cells. However, these technologies have ultra-low sequencing coverage (<0.5× per cell) which has limited their use to the analysis of large copy-number aberrations (CNAs) in individual cells. While CNAs are useful markers in cancer studies, single-nucleotide mutations are equally important, both in cancer studies and in other applications. However, ultra-low coverage sequencing yields single-nucleotide mutation data that are too sparse for current single-cell analysis methods. Results We introduce SBMClone, a method to infer clusters of cells, or clones, that share groups of somatic single-nucleotide mutations. SBMClone uses a stochastic block model to overcome sparsity in ultra-low coverage single-cell sequencing data, and we show that SBMClone accurately infers the true clonal composition on simulated datasets with coverage at low as 0.2×. We applied SBMClone to single-cell whole-genome sequencing data from two breast cancer patients obtained using two different sequencing technologies. On the first patient, sequenced using the 10X Genomics CNV solution with sequencing coverage ≈0.03×, SBMClone recovers the major clonal composition when incorporating a small amount of additional information. On the second patient, where pre- and post-treatment tumor samples were sequenced using DOP-PCR with sequencing coverage ≈0.5×, SBMClone shows that tumor cells are present in the post-treatment sample, contrary to published analysis of this dataset. Availability and implementation SBMClone is available on the GitHub repository https://github.com/raphael-group/SBMClone. Supplementary information Supplementary data are available at Bioinformatics online.
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Zhao, Peng, Zenglin Xu, Junjie Chen, Yazhou Ren, and Irwin King. "Single Cell Self-Paced Clustering with Transcriptome Sequencing Data." International Journal of Molecular Sciences 23, no. 7 (March 31, 2022): 3900. http://dx.doi.org/10.3390/ijms23073900.

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Single cell RNA sequencing (scRNA-seq) allows researchers to explore tissue heterogeneity, distinguish unusual cell identities, and find novel cellular subtypes by providing transcriptome profiling for individual cells. Clustering analysis is usually used to predict cell class assignments and infer cell identities. However, the performance of existing single-cell clustering methods is extremely sensitive to the presence of noise data and outliers. Existing clustering algorithms can easily fall into local optimal solutions. There is still no consensus on the best performing method. To address this issue, we introduce a single cell self-paced clustering (scSPaC) method with F-norm based nonnegative matrix factorization (NMF) for scRNA-seq data and a sparse single cell self-paced clustering (sscSPaC) method with l21-norm based nonnegative matrix factorization for scRNA-seq data. We gradually add single cells from simple to complex to our model until all cells are selected. In this way, the influences of noisy data and outliers can be significantly reduced. The proposed method achieved the best performance on both simulation data and real scRNA-seq data. A case study about human clara cells and ependymal cells scRNA-seq data clustering shows that scSPaC is more advantageous near the clustering dividing line.
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Dissertations / Theses on the topic "Single cell sequencing data"

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Ross, Edith. "Inferring tumour evolution from single-cell and multi-sample data." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274604.

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Tumour development has long been recognised as an evolutionary process during which cells accumulate mutations and evolve into a mix of genetically distinct cell subpopulations. The resulting genetic intra-tumour heterogeneity poses a major challenge to cancer therapy, as it increases the chance of drug resistance. To study tumour evolution in more detail, reliable approaches to infer the life histories of tumours are needed. This dissertation focuses on computational methods for inferring trees of tumour evolution from single-cell and multi-sample sequencing data. Recent advances in single-cell sequencing technologies have promised to reveal tumour heterogeneity at a much higher resolution, but single-cell sequencing data is inherently noisy, making it unsuitable for analysis with classic phylogenetic methods. The first part of the dissertation describes OncoNEM, a novel probabilistic method to infer clonal lineage trees from noisy single nucleotide variants of single cells. Simulation studies are used to validate the method and to compare its performance to that of other methods. Finally, OncoNEM is applied in two case studies. In the second part of the dissertation, a comprehensive collection of existing multi-sample approaches is used to infer the phylogenies of metastatic breast cancers from ten patients. In particular, shallow whole-genome, whole exome and targeted deep sequencing data are analysed. The inference methods comprise copy number and point mutation based approaches, as well as a method that utilises a combination of the two. To improve the copy number based inference, a novel allele-specific multi-sample segmentation algorithm is presented. The results are compared across methods and data types to assess the reliability of the different methods. In summary, this thesis presents substantial methodological advances to understand tumour evolution from genomic profiles of single cells or related bulk samples.
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Salehi, Sohrab. "dd-PyClone : improving clonal subpopulation inference from single cells and bulk sequencing data." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/56179.

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Improving our understanding of intra-tumour heterogeneity in cancer has important clinical implications, including an opportunity to understand mechanisms behind relapses and drug resistance. Next generation bulk sequencing is a mature tech- nology that has been used to study subclonal tumour populations at an aggregate level. Inference of populations from bulk sequencing requires sophisticated com- putational deconvolution methods. An alternative is to identify populations directly with single cell sequencing. However, single cell sequencing is a very error-prone process, and this impedes its ability to completely replace bulk sequencing for now. In this work we present dd-PyClone, a statistical model to combine single cell and bulk sequencing data to study clonal subpopulation architecture and improve clustering assignment and cellular prevalence estimates of a set of genomic loci. We introduce a single nucleotide variant and copy number aberration aware genotype simulation scheme based on a phylogenetic tree, termed the Generalized Dollo model. This model is an improvement over previous genotype generator models in that it also accounts for the evolutionary process before a rare event (here the single nucleotide variant) occurs. We show that incorporating genomic loci co-occurrence patterns from single cell sequencing studies in inferring clonal subpopulation structure from bulk se- quencing data is beneficial. Our method outperforms existing methods in simula- tion studies and performs comparably in real dataset benchmarking. We also show that our method is fairly robust as to the choice of hyperparameters and performs reasonably in presence of noise. We hope that our method will further the under- standing of the evolutionary basis of cancer.
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Lavagi, Ilaria Verfasser], and Eckhard [Akademischer Betreuer] [Wolf. "Analysis of blastomere of bovine embryos during genome activation by evaluation of single-cell RNA sequencing data / Ilaria Lavagi ; Betreuer: Eckhard Wolf." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2018. http://d-nb.info/1167160541/34.

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Bampalikis, Dimitrios. "Recognizing biological and technical differences in scRNAseq : A comparison of two protocols." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-366169.

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Recent advances in sequencing technology have given access to information extracted on a single cell level. Single cell RNA sequencing enables for transcriptomes to be sequenced, allowing for studies within and between cell types. A recently developed protocol, based on Smart-seq2, and the Proximity ligation essay, allows for the detection of protein data from single cells, in parallel with RNA. The combination of the transcriptomic and proteomic data will enhance researchers’ ability to explore cell states. In this study, we are comparing a new pulldown protocol with the widely-used Smart-seq2, as well as against FACS sorted cells. Our results show differences in the RNA sequenced between the two protocols, as well the prediction of cell cycle state based on their data. Using RNA extracted from the pulldown protocol in different time points, we also calculate the direction of development for the cells. We expect that the incorporation of proteomic data will shed light to relevant biological questions related to the cell function.
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Ronen, Jonathan. "Integrative analysis of data from multiple experiments." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21612.

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Auf die Entwicklung der Hochdurchsatz-Sequenzierung (HTS) folgte eine Reihe von speziellen Erweiterungen, die erlauben verschiedene zellbiologischer Aspekte wie Genexpression, DNA-Methylierung, etc. zu messen. Die Analyse dieser Daten erfordert die Entwicklung von Algorithmen, die einzelne Experimenteberücksichtigen oder mehrere Datenquellen gleichzeitig in betracht nehmen. Der letztere Ansatz bietet besondere Vorteile bei Analyse von einzelligen RNA-Sequenzierung (scRNA-seq) Experimenten welche von besonders hohem technischen Rauschen, etwa durch den Verlust an Molekülen durch die Behandlung geringer Ausgangsmengen, gekennzeichnet sind. Um diese experimentellen Defizite auszugleichen, habe ich eine Methode namens netSmooth entwickelt, welche die scRNA-seq-Daten entrascht und fehlende Werte mittels Netzwerkdiffusion über ein Gennetzwerk imputiert. Das Gennetzwerk reflektiert dabei erwartete Koexpressionsmuster von Genen. Unter Verwendung eines Gennetzwerks, das aus Protein-Protein-Interaktionen aufgebaut ist, zeige ich, dass netSmooth anderen hochmodernen scRNA-Seq-Imputationsmethoden bei der Identifizierung von Blutzelltypen in der Hämatopoese, zur Aufklärung von Zeitreihendaten unter Verwendung eines embryonalen Entwicklungsdatensatzes und für die Identifizierung von Tumoren der Herkunft für scRNA-Seq von Glioblastomen überlegen ist. netSmooth hat einen freien Parameter, die Diffusionsdistanz, welche durch datengesteuerte Metriken optimiert werden kann. So kann netSmooth auch dann eingesetzt werden, wenn der optimale Diffusionsabstand nicht explizit mit Hilfe von externen Referenzdaten optimiert werden kann. Eine integrierte Analyse ist auch relevant wenn multi-omics Daten von mehrerer Omics-Protokolle auf den gleichen biologischen Proben erhoben wurden. Hierbei erklärt jeder einzelne dieser Datensätze nur einen Teil des zellulären Systems, während die gemeinsame Analyse ein vollständigeres Bild ergibt. Ich entwickelte eine Methode namens maui, um eine latente Faktordarstellungen von multiomics Daten zu finden.
The development of high throughput sequencing (HTS) was followed by a swarm of protocols utilizing HTS to measure different molecular aspects such as gene expression (transcriptome), DNA methylation (methylome) and more. This opened opportunities for developments of data analysis algorithms and procedures that consider data produced by different experiments. Considering data from seemingly unrelated experiments is particularly beneficial for Single cell RNA sequencing (scRNA-seq). scRNA-seq produces particularly noisy data, due to loss of nucleic acids when handling the small amounts in single cells, and various technical biases. To address these challenges, I developed a method called netSmooth, which de-noises and imputes scRNA-seq data by applying network diffusion over a gene network which encodes expectations of co-expression patterns. The gene network is constructed from other experimental data. Using a gene network constructed from protein-protein interactions, I show that netSmooth outperforms other state-of-the-art scRNA-seq imputation methods at the identification of blood cell types in hematopoiesis, as well as elucidation of time series data in an embryonic development dataset, and identification of tumor of origin for scRNA-seq of glioblastomas. netSmooth has a free parameter, the diffusion distance, which I show can be selected using data-driven metrics. Thus, netSmooth may be used even in cases when the diffusion distance cannot be optimized explicitly using ground-truth labels. Another task which requires in-tandem analysis of data from different experiments arises when different omics protocols are applied to the same biological samples. Analyzing such multiomics data in an integrated fashion, rather than each data type (RNA-seq, DNA-seq, etc.) on its own, is benefitial, as each omics experiment only elucidates part of an integrated cellular system. The simultaneous analysis may reveal a comprehensive view.
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Büttner, Maren [Verfasser], Fabian J. [Akademischer Betreuer] Theis, Julien [Gutachter] Gagneur, Fabian J. [Gutachter] Theis, and Peter V. [Gutachter] Kharchenko. "Statistical data integration for single-cell RNA-sequencing - batch effect correction and lineage inference / Maren Büttner ; Gutachter: Julien Gagneur, Fabian J. Theis, Peter V. Kharchenko ; Betreuer: Fabian J. Theis." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/119244194X/34.

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Johnson, Travis Steele. "Integrative approaches to single cell RNA sequencing analysis." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586960661272666.

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Borgström, Erik. "Technologies for Single Cell Genome Analysis." Doctoral thesis, KTH, Genteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181059.

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During the last decade high throughput DNA sequencing of single cells has evolved from an idea to one of the most high profile fields of research. Much of this development has been possible due to the dramatic reduction in costs for massively parallel sequencing. The four papers included in this thesis describe or evaluate technological advancements for high throughput DNA sequencing of single cells and single molecules. As the sequencing technologies improve, more samples are analyzed in parallel. In paper 1, an automated procedure for preparation of samples prior to massively parallel sequencing is presented. The method has been applied to several projects and further development by others has enabled even higher sample throughputs. Amplification of single cell genomes is a prerequisite for sequence analysis. Paper 2 evaluates four commercially available kits for whole genome amplification of single cells. The results show that coverage of the genome differs significantly among the protocols and as expected this has impact on the downstream analysis. In Paper 3, single cell genotyping by exome sequencing is used to confirm the presence of fat cells derived from donated bone marrow within the recipients’ fat tissue. Close to hundred single cells were exome sequenced and a subset was validated by whole genome sequencing. In the last paper, a new method for phasing (i.e. determining the physical connection of variant alleles) is presented. The method barcodes amplicons from single molecules in emulsion droplets. The barcodes can then be used to determine which variants were present on the same original DNA molecule. The method is applied to two variable regions in the bacterial 16S gene in a metagenomic sample. Thus, two of the papers (1 and 4) present development of new methods for increasing the throughput and information content of data from massively parallel sequencing. Paper 2 evaluates and compares currently available methods and in paper 3, a biological question is answered using some of these tools.

QC 20160127

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Raoux, Corentin. "Review and Analysis of single-cell RNA sequencing cell-type identification and annotation tools." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297852.

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Single-cell RNA-sequencing makes possible to study the gene expression at the level of individual cells. However, one of the main challenges of the single-cell RNA-sequencing analysis today, is the identification and annotation of cell types. The current method consists in manually checking the expression of genes using top differentially expressed genes and comparing them with related cell-type markers available in scientific publications. It is therefore time-consuming and labour intensive. Nevertheless, in the last two years,numerous automatic cell-type identification and annotation tools which use different strategies have been created. But, the lack of specific comparisons of those tools in the literature and especially for immuno-oncologic and oncologic purposes makes difficult for laboratories and companies to know objectively what are the best tools for annotating cell types. In this project, a review of the current tools and an evaluation of R tools were carried out.The annotation performance, the computation time and the ease of use were assessed. After this preliminary results, the best selected R tools seem to be ClustifyR (fast and rather precise) and SingleR (precise) for the correlation-based tools, and SingleCellNet (precise and rather fast) and scPred (precise but a lot of cell types remains unassigned) for the supervised classificationtools. Finally, for the marker-based tools, MAESTRO and SCINA are rather robust if they are provided with high quality markers.
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Kindblom, Marie, and Hakim Ezeddin Al. "Phylogenetic fatemapping: estimating allelic dropout probability in single cell genomic sequencing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186453.

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Single-cell genomic sequencing is a rapidly developing field that will play a vital role in human biology and science in the future. As of now, next-generation sequencing is accelerating in speed and decreasing in cost more quickly than Moore's law. Studies have shown that all cells in the human body have with very high probability a unique genomic signature, due to the somatic evolution which have accumulated mutations starting from the zygotic state. The possible reconstruction of phylogenetic lineage trees would be of vital importance to several fields in medicine, such as the stem cell research field. However, state-of-the-art methods for amplification such as WGA currently suffers from extensive allelic dropout which is troublesome when reconstructing phylogenetic trees. We have constructed a statistical model that can be used to predict site specific allelic dropout. Our results suggests that logistic regression is a suitable method for modelling allelic dropout, and that there is a non-linear relationship between the read depth and distance.
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Books on the topic "Single cell sequencing data"

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Suzuki, Yutaka, ed. Single Molecule and Single Cell Sequencing. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6037-4.

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Yu, Buwei, Jiaqiang Zhang, Yiming Zeng, Li Li, and Xiangdong Wang, eds. Single-cell Sequencing and Methylation. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4494-1.

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Wang, Xiangdong, ed. Single Cell Sequencing and Systems Immunology. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9753-5.

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Loos, Carolin. Analysis of Single-Cell Data. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13234-7.

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Yuan, Guo-Cheng, ed. Computational Methods for Single-Cell Data Analysis. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9057-3.

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Mallick, Himel, Lingling An, Mengjie Chen, Pei Wang, and Ni Zhao, eds. Methods for Single-Cell and Microbiome Sequencing Data. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88976-280-4.

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Chen, Geng, Zhichao Liu, and Cheng Peng, eds. Multimodal and Integrative Analysis of Single-Cell or Bulk Sequencing Data. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88966-668-3.

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Yang, Jialiang, Liao Bo, Tuo Zhang, and Yifei Xu, eds. Bioinformatics Analysis of Single Cell Sequencing Data and Applications in Precision Medicine. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-528-3.

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Suzuki, Yutaka. Single Molecule and Single Cell Sequencing. Springer, 2019.

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Wang, Xiangdong. Single Cell Sequencing and Systems Immunology. Springer, 2016.

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Book chapters on the topic "Single cell sequencing data"

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Gao, Shan. "Data Analysis in Single-Cell Transcriptome Sequencing." In Methods in Molecular Biology, 311–26. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7717-8_18.

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Sagar, Josip Stefan Herman, John Andrew Pospisilik, and Dominic Grün. "High-Throughput Single-Cell RNA Sequencing and Data Analysis." In Methods in Molecular Biology, 257–83. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7768-0_15.

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Vermeersch, Lieselotte, Abbas Jariani, Jana Helsen, Benjamin M. Heineike, and Kevin J. Verstrepen. "Single-Cell RNA Sequencing in Yeast Using the 10× Genomics Chromium Device." In Methods in Molecular Biology, 3–20. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2257-5_1.

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AbstractSingle-cell RNA sequencing (scRNA-seq) is emerging as an essential technique for studying the physiology of individual cells in populations. Although well-established and optimized for mammalian cells, research of microorganisms has been faced with major technical challenges for using scRNA-seq, because of their rigid cell wall, smaller cell size and overall lower total RNA content per cell. Here, we describe an easy-to-implement adaptation of the protocol for the yeast Saccharomyces cerevisiae using the 10× Genomics platform, originally optimized for mammalian cells. Introducing Zymolyase, a cell wall–digesting enzyme, to one of the initial steps of single-cell droplet formation allows efficient in-droplet lysis of yeast cells, without affecting the droplet emulsion and further sample processing. In addition, we also describe the downstream data analysis, which combines established scRNA-seq analysis protocols with specific adaptations for yeast, and R-scripts for further secondary analysis of the data.
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Tran, Duc, Frederick C. Harris, Bang Tran, Nam Sy Vo, Hung Nguyen, and Tin Nguyen. "Single-Cell RNA Sequencing Data Imputation Using Deep Neural Network." In Advances in Intelligent Systems and Computing, 403–10. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70416-2_52.

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Wang, Zuoheng, and Xiting Yan. "Computational and Statistical Methods for Single-Cell RNA Sequencing Data." In Springer Handbooks of Computational Statistics, 3–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65902-1_1.

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Sagar and Dominic Grün. "Lineage Inference and Stem Cell Identity Prediction Using Single-Cell RNA-Sequencing Data." In Computational Stem Cell Biology, 277–301. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9224-9_13.

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Ulbrich, Jannes, Vadir Lopez-Salmeron, and Ian Gerrard. "BD Rhapsody™ Single-Cell Analysis System Workflow: From Sample to Multimodal Single-Cell Sequencing Data." In Methods in Molecular Biology, 29–56. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2756-3_2.

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Lei, Haoyun, Bochuan Lyu, E. Michael Gertz, Alejandro A. Schäffer, Xulian Shi, Kui Wu, Guibo Li, et al. "Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data." In Lecture Notes in Computer Science, 174–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17083-7_11.

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Li, Ronnie Y., Wenjing Ma, and Zhaohui S. Qin. "Approaches to Marker Gene Identification from Single-Cell RNA-Sequencing Data." In Springer Handbooks of Computational Statistics, 71–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65902-1_4.

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Bahonar, Sajedeh, and Hesam Montazeri. "Somatic Single-Nucleotide Variant Calling from Single-Cell DNA Sequencing Data Using SCAN-SNV." In Variant Calling, 267–77. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2293-3_17.

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Conference papers on the topic "Single cell sequencing data"

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Ciccolella, Simone, Murray D. Patterson, Paola Bonizzoni, and Gianluca Della Vedova. "Effective Clustering for Single Cell Sequencing Cancer Data." In BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3307339.3342149.

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Zare, Fatima, Jacob Stark, and Sheida Nabavi. "Copy number variation detection using single cell sequencing data." In BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459930.3469556.

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Zhang, Wenjuan, William Yang, John Talburt, Sherman Weissman, and Mary Qu Yang. "Missing Value Recovery for Single Cell RNA Sequencing Data." In 2021 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2021. http://dx.doi.org/10.1109/csci54926.2021.00129.

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Tasoulis, Sotiris K., Aristidis G. Vrahatis, Spiros V. Georgakopoulos, and Vassilis P. Plagianakos. "Visualizing High-dimensional single-cell RNA-sequencing data through multiple Random Projections." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622170.

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Vrahatis, Aristidis G., Georgios N. Dimitrakopoulos, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, and Vassilis P. Plagianakos. "Single-cell regulatory network inference and clustering from high-dimensional sequencing data." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006016.

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Wang, Tianyu, and Sheida Nabavi. "Differential gene expression analysis in single-cell RNA sequencing data." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217650.

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Bai, Litai, Yuan Zhu, and Ming Yi. "Clustering Single-Cell RNA Sequencing Data by Deep Learning Algorithm." In 2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB). IEEE, 2021. http://dx.doi.org/10.1109/icbcb52223.2021.9459219.

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Liu, Chenliang, Yuan Zhu, and Houwang Zhang. "Cellular Similarity based Imputation for Single cell RNA Sequencing Data." In ICBBT '21: 2021 13th International Conference on Bioinformatics and Biomedical Technology. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3473258.3473269.

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Tran, Duc, Hung Nguyen, Frederick C. Harris, and Tin Nguyen. "Single-cell RNA sequencing data imputation using similarity preserving network." In 2021 13th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2021. http://dx.doi.org/10.1109/kse53942.2021.9648794.

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Wang, Tianyu, Bingjun Li, and Sheida Nabavi. "Single-cell RNA sequencing data clustering using graph convolutional networks." In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669529.

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Reports on the topic "Single cell sequencing data"

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Savaldi-Goldstein, Sigal, and Todd C. Mockler. Precise Mapping of Growth Hormone Effects by Cell-Specific Gene Activation Response. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7699849.bard.

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Plant yield largely depends on a complex interplay and feedback mechanisms of distinct hormonal pathways. Over the past decade great progress has been made in elucidating the global molecular mechanisms by which each hormone is produced and perceived. However, our knowledge of how interactions between hormonal pathways are spatially and temporally regulated remains rudimentary. For example, we have demonstrated that although the BR receptor BRI1 is widely expressed, the perception of BRs in epidermal cells is sufficient to control whole-organ growth. Supported by additional recent works, it is apparent that hormones are acting in selected cells of the plant body to regulate organ growth, and furthermore, that local cell-cell communication is an important mechanism. In this proposal our goals were to identify the global profile of translated genes in response to BR stimulation and depletion in specific tissues in Arabidopsis; determine the spatio-temporal dependency of BR response on auxin transport and signaling and construct an interactive public website that will provide an integrated analysis of the data set. Our technology incorporated cell-specific polysome isolation and sequencing using the Solexa technology. In the first aim, we generated and confirmed the specificity of novel transgenic lines expressing tagged ribosomal protein in various cell types in the Arabidopsis primary root. We next crossed these lines to lines with targeted expression of BRI1 in the bri1 background. All lines were treated with BRs for two time points. The RNA-seq of their corresponding immunopurified polysomal RNA is nearly completed and the bioinformatic analysis of the data set will be completed this year. Followed, we will construct an interactive public website (our third aim). In the second aim we started revealing how spatio-temporalBR activity impinges on auxin transport in the Arabidopsis primary root. We discovered the unexpected role of BRs in controlling the expression of specific auxin efflux carriers, post-transcriptionally (Hacham et al, 2012). We also showed that this regulation depends on the specific expression of BRI1 in the epidermis. This complex and long term effect of BRs on auxin transport led us to focus on high resolution analysis of the BR signaling per se. Taking together, our ongoing collaboration and synergistic expertise (hormone action and plant development (IL) and whole-genome scale data analysis (US)) enabled the establishment of a powerful system that will tell us how distinct cell types respond to local and systemic BR signal. BR research is of special agriculture importance since BR application and BR genetic modification have been shown to significantly increase crop yield and to play an important role in plant thermotolerance. Hence, our integrated dataset is valuable for improving crop traits without unwanted impairment of unrelated pathways, for example, establishing semi-dwarf stature to allow increased yield in high planting density, inducing erect leaves for better light capture and consequent biomass increase and plant resistance to abiotic stresses.
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Harouaka, Ramdane. Platform for Single-Cell Dual RNA Sequencing of Host-Pathogen Interactions. Office of Scientific and Technical Information (OSTI), October 2021. http://dx.doi.org/10.2172/1832283.

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Fung, N. DNA sequencing with capillary electrophoresis and single cell analysis with mass spectrometry. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/348902.

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Palmer, Guy, Varda Shkap, Wendy Brown, and Thea Molad. Control of bovine anaplasmosis: cytokine enhancement of vaccine efficacy. United States Department of Agriculture, March 2007. http://dx.doi.org/10.32747/2007.7695879.bard.

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Anaplasmosis an arthropod-born disease of cattle caused by the rickettsia Anaplasma marginale and is an impediment to efficient production of healthy livestock in both Israel and the United States. Currently the only effective vaccines are derived from the blood of infected cattle. The risk of widespread transmission of both known and newly emergent pathogens has prevented licensure of live blood-based vaccines in the U.S. and is a major concern for their continued use in Israel. Consequently development of a safe, effective vaccine is a high priority. In this collaborative project we focused on two approaches to vaccine development. The first focused o n improving antigen delivery to livestock and specifically examined how DNA vaccines could be improved to enhance priming and expansion of the immune response. This research resulted in development and testing of two novel vaccine delivery systems--one that targeted antigen spread among dendritic cells (the key cell in priming immune responses and a follow-on construct that also specifically targeted antigen to the endosomal-lysosomal compartment the processing organelle within the dendritic cell that directs vaccine antigen to the MHC class ll-CD4* T cell priming pathway). The optimized construct targeting vaccine antigen to the dendritic cell MHC class II pathway was tested for ability to prime A. marginale specific immune responses in outbred cattle. The results demonstrated both statistically significant effects of priming with a single immunization, continued expansion of the primary immune response including development of high affinity lgG antibodies and rapid recall of the memory response following antigen challenge. This portion of the study represented a significant advance in vaccine delivery for livestock. Importantly the impact of these studies is not limited to A. marginale a s the targeting motifs are optimized for cattle and can be adapted to other cattle vaccinations by inserting a relevant pathogen-specific antigen. The second approach (which represented an addition to the project for which approval was requested as part of the first annual report) was a comparative approach between A . marginale and the Israel A . centrale vaccines train. This addition was requested as studies on Major Surface Protein( MSP)- 2 have shown that this antigen is highly antigenically variable and presented solely as a "static vaccine" antigen does not give cross-strain immunity. In contrast A. . centrale is an effective vaccine which Kimron Veterinary institute has used in the field in Israel for over 50 years. Taking advantage of this expertise, a broad comparison of wild type A. marginale and vaccine strain was initiated. These studies revealed three primary findings: i) use of the vaccine is associated with superinfection, but absence of clinical disease upon superinfection with A. marginale; ii) the A. centrale vaccine strain is not only less virulent but transmission in competent in Dermacentor spp. ticks; and iii) some but not all MSPs are conserved in basic orthologous structure but there are significant polymorphisms among the strains. These studies clearly indicated that there are statistically significant differences in biology (virulence and transmission) and provide a clear path for mapping of biology with the genomes. Based on these findings, we initiated complete genome sequencing of the Israel vaccine strain (although not currently funded by BARD) and plant to proceed with a comparative genomics approach using already sequenced wild-type A. marginale. These findings and ongoing collaborative research tie together filed vaccine experience with new genomic data, providing a new approach to vaccine development against a complex pathogen.
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Hawkins, Brian T., and Sonia Grego. A Better, Faster Road From Biological Data to Human Health: A Systems Biology Approach for Engineered Cell Cultures. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.rb.0015.1706.

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Traditionally, the interactions of drugs and toxicants with human tissue have been investigated in a reductionist way—for example, by focusing on specific molecular targets and using single-cell-type cultures before testing compounds in whole organisms. More recently, “systems biology” approaches attempt to enhance the predictive value of in vitro biological data by adopting a comprehensive description of biological systems and using computational tools that are sophisticated enough to handle the complexity of these systems. However, the utility of computational models resulting from these efforts completely relies on the quality of the data used to construct them. Here, we propose that recent advances in the development of bioengineered, three-dimensional, multicellular constructs provide in vitro data of sufficient complexity and physiological relevance to be used in predictive systems biology models of human responses. Such predictive models are essential to maximally leveraging these emerging bioengineering technologies to improve both therapeutic development and toxicity risk assessment. This brief outlines the opportunities presented by emerging technologies and approaches for the acceleration of drug development and toxicity testing, as well as the challenges lying ahead for the field.
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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.

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Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.
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Bacharach, Eran, and Sagar Goyal. Generation of Avian Pneumovirus Modified Clones for the Development of Attenuated Vaccines. United States Department of Agriculture, November 2008. http://dx.doi.org/10.32747/2008.7696541.bard.

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Abstract (one page maximum, single spaced), include: List the original objectives, as defined in the approved proposal, and any revisions made at the beginning or during the course of project: The main goal described in our original proposal has been the development of a molecular infectious clone of the avian metapneumovirus subtype B (aMPV-B) and the modification of this clone to create mutated viruses for the development of attenuated vaccines. The Achievements and Appendix/Part I sections of this report describes the accomplishments in creating such a molecular clone. These sections also contain the results of a longitudinal study that we made in Israel, demonstrating the infiltration of field strains of aMPV into vaccinated flocks and emphasizing the need for the development of better vaccines. We also describe our unexpected findings regarding the ability of aMPV to establish persistent infection in cell cultures. Although this direction of research was not described in the original proposal we feel that it is highly important for the understanding of aMPV pathogenesis. For example, this direction has provided us with evidence showing that aMPV replication can augment influenza replication. Moreover, we observed that viruses that were produced from chronically-infected cells show reduced ciliostasis. Accordingly, we carried vaccination trials using such viruses. In the original grant proposal we also offered that the American lab will clone and express immunomodulators in the context of an aMPV -based replicon that the Israeli lab has generated. However, as we reported in our annual reports, further analysis of this replicon by the Israeli lab has revealed that the level of expression achieved by this vehicle is relatively poor; thus, the American lab has focused on sequencing the genomes of different aMPV-C isolates that differ in their virulence (including vaccine strains). Achievements and Appendix/Part II sections of this report include the summary of this effort. Background to the topic: The aMPVs belong to the paramyxoviridae family and cause mild to severe respiratory tract diseases mainly in turkeys and also in chickens. Four aMPV subgroups, A, B, C and D, have been characterized; in Israel aMPV-A and B are the common subtypes while in the USA type C is the prevalent one. Although vaccine strains do exist for aMPVs, they do not always provide full protection against virulent strains and the vaccines themselves may induce disease to some extent. Improved vaccines against aMPV are needed, to achieve better protection of the poultry industry against this pathogen. Major conclusions, solutions, achievements: We isolated aMPV-B from a diseased flock and accomplished the sequencing and cloning of its full-genome. In addition, we cloned the four genes encoding the viral replicase. These should serve as the platform for generation of modified aMPV-Bs from molecular clones. We also identified aMPVs that are attenuated in respect to their ciliostatic activity and accordingly showed the potential of such viruses as vaccine strains. For aMPV-C, the different mutations scattered along the genome of different isolates with varied virulence have been determined. Implications, both scientific and agricultural: The newly identified pattern of mutations in attenuated strains will allow better understanding of the pathogenicity of aMPV and the generation of aMPV molecular clones, together with isolation of strains with attenuated ciliostatic activity should generate improved vaccine strains Abstract (one page maximum, single spaced), include: List the original objectives, as defined in the approved proposal, and any revisions made at the beginning or during the course of project: The main goal described in our original proposal has been the development of a molecular infectious clone of the avian metapneumovirus subtype B (aMPV-B) and the modification of this clone to create mutated viruses for the development of attenuated vaccines. The Achievements and Appendix/Part I sections of this report describes the accomplishments in creating such a molecular clone. These sections also contain the results of a longitudinal study that we made in Israel, demonstrating the infiltration of field strains of aMPV into vaccinated flocks and emphasizing the need for the development of better vaccines. We also describe our unexpected findings regarding the ability of aMPV to establish persistent infection in cell cultures. Although this direction of research was not described in the original proposal we feel that it is highly important for the understanding of aMPV pathogenesis. For example, this direction has provided us with evidence showing that aMPV replication can augment influenza replication. Moreover, we observed that viruses that were produced from chronically-infected cells show reduced ciliostasis. Accordingly, we carried vaccination trials using such viruses. In the original grant proposal we also offered that the American lab will clone and express immunomodulators in the context of an aMPV -based replicon that the Israeli lab has generated. However, as we reported in our annual reports, further analysis of this replicon by the Israeli lab has revealed that the level of expression achieved by this vehicle is relatively poor; thus, the American lab has focused on sequencing the genomes of different aMPV-C isolates that differ in their virulence (including vaccine strains). Achievements and Appendix/Part II sections of this report include the summary of this effort. Background to the topic: The aMPVs belong to the paramyxoviridae family and cause mild to severe respiratory tract diseases mainly in turkeys and also in chickens. Four aMPV subgroups, A, B, C and D, have been characterized; in Israel aMPV-A and B are the common subtypes while in the USA type C is the prevalent one. Although vaccine strains do exist for aMPVs, they do not always provide full protection against virulent strains and the vaccines themselves may induce disease to some extent. Improved vaccines against aMPV are needed, to achieve better protection of the poultry industry against this pathogen. Major conclusions, solutions, achievements: We isolated aMPV-B from a diseased flock and accomplished the sequencing and cloning of its full-genome. In addition, we cloned the four genes encoding the viral replicase. These should serve as the platform for generation of modified aMPV-Bs from molecular clones. We also identified aMPVs that are attenuated in respect to their ciliostatic activity and accordingly showed the potential of such viruses as vaccine strains. For aMPV-C, the different mutations scattered along the genome of different isolates with varied virulence have been determined. Implications, both scientific and agricultural: The newly identified pattern of mutations in attenuated strains will allow better understanding of the pathogenicity of aMPV and the generation of aMPV molecular clones, together with isolation of strains with attenuated ciliostatic activity should generate improved vaccine strains.
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Cytryn, Eddie, Mark R. Liles, and Omer Frenkel. Mining multidrug-resistant desert soil bacteria for biocontrol activity and biologically-active compounds. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598174.bard.

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Control of agro-associated pathogens is becoming increasingly difficult due to increased resistance and mounting restrictions on chemical pesticides and antibiotics. Likewise, in veterinary and human environments, there is increasing resistance of pathogens to currently available antibiotics requiring discovery of novel antibiotic compounds. These drawbacks necessitate discovery and application of microorganisms that can be used as biocontrol agents (BCAs) and the isolation of novel biologically-active compounds. This highly-synergistic one year project implemented an innovative pipeline aimed at detecting BCAs and associated biologically-active compounds, which included: (A) isolation of multidrug-resistant desert soil bacteria and root-associated bacteria from medicinal plants; (B) invitro screening of bacterial isolates against known plant, animal and human pathogens; (C) nextgeneration sequencing of isolates that displayed antagonistic activity against at least one of the model pathogens and (D) in-planta screening of promising BCAs in a model bean-Sclerotiumrolfsii system. The BCA genome data were examined for presence of: i) secondary metabolite encoding genes potentially linked to the anti-pathogenic activity of the isolates; and ii) rhizosphere competence-associated genes, associated with the capacity of microorganisms to successfully inhabit plant roots, and a prerequisite for the success of a soil amended BCA. Altogether, 56 phylogenetically-diverse isolates with bioactivity against bacterial, oomycete and fungal plant pathogens were identified. These strains were sent to Auburn University where bioassays against a panel of animal and human pathogens (including multi-drug resistant pathogenic strains such as A. baumannii 3806) were conducted. Nineteen isolates that showed substantial antagonistic activity against at least one of the screened pathogens were sequenced, assembled and subjected to bioinformatics analyses aimed at identifying secondary metabolite-encoding and rhizosphere competence-associated genes. The genome size of the bacteria ranged from 3.77 to 9.85 Mbp. All of the genomes were characterized by a plethora of secondary metabolite encoding genes including non-ribosomal peptide synthase, polyketidesynthases, lantipeptides, bacteriocins, terpenes and siderophores. While some of these genes were highly similar to documented genes, many were unique and therefore may encode for novel antagonistic compounds. Comparative genomic analysis of root-associated isolates with similar strains not isolated from root environments revealed genes encoding for several rhizospherecompetence- associated traits including urea utilization, chitin degradation, plant cell polymerdegradation, biofilm formation, mechanisms for iron, phosphorus and sulfur acquisition and antibiotic resistance. Our labs are currently writing a continuation of this feasibility study that proposes a unique pipeline for the detection of BCAs and biopesticides that can be used against phytopathogens. It will combine i) metabolomic screening of strains from our collection that contain unique secondary metabolite-encoding genes, in order to isolate novel antimicrobial compounds; ii) model plant-based experiments to assess the antagonistic capacities of selected BCAs toward selected phytopathogens; and iii) an innovative next-generation-sequencing based method to monitor the relative abundance and distribution of selected BCAs in field experiments in order to assess their persistence in natural agro-environments. We believe that this integrated approach will enable development of novel strains and compounds that can be used in large-scale operations.
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Fahima, Tzion, and Jorge Dubcovsky. Map-based cloning of the novel stripe rust resistance gene YrG303 and its use to engineer 1B chromosome with multiple beneficial traits. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598147.bard.

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Research problem: Bread wheat (Triticumaestivum) provides approximately 20% of the calories and proteins consumed by humankind. As the world population continues to increase, it is necessary to improve wheat yields, increase grain quality, and minimize the losses produced by biotic and abiotic stresses. Stripe rust, caused by Pucciniastriiformisf. sp. tritici(Pst), is one of the most destructive diseases of wheat. The new pathogen races are more virulent and aggressive than previous ones and have produced large economic losses. A rich source for stripe-rust resistance genes (Yr) was found in wild emmer wheat populations from Israel. Original Project goals: Our long term goal is to identify, map, clone, characterize and deploy in breeding, novel wild emmer Yr genes, and combine them with multiple beneficial traits. The current study was aiming to map and clone YrG303 and Yr15, located on chromosome 1BS and combine them with drought resistance and grain quality genes. Positional cloning of YrG303/Yr15: Fine mapping of these genes revealed that YrG303 is actually allelic to Yr15. Fine genetic mapping using large segregating populations resulted in reduction of the genetic interval spanning Yr15 to less than 0.1 cM. Physical mapping of the YrG303/Yr15 locus was based on the complete chromosome 1BS physical map of wheat constructed by our group. Screening of 1BS BAC library with Yr15 markers revealed a long BAC scaffold covering the target region. The screening of T. dicoccoidesaccession-specific BAC library with Yr15 markers resulted in direct landing on the target site. Sequencing of T. dicoccoidesBAC clones that cover the YrG303/Yr15 locus revealed a single candidate gene (CG) with conserved domains that may indicate a role in disease resistance response. Validation of the CG was carried out using EMS mutagenesis (loss-of- function approach). Sequencing of the CG in susceptible yr15/yrG303 plants revealed three independent mutants that harbour non-functional yr15/yrG303 alleles within the CG conserved domains, and therefore validated its function as a Pstresistance gene. Evaluation of marker-assisted-selection (MAS) for Yr15. Introgressions of Yr15 into cultivated wheat are widely used now. Recently, we have shown that DNA markers linked to Yr15 can be used as efficient tools for introgression of Yr15 into cultivated wheat via MAS. The developed markers were consistent and polymorphic in all 34 tested introgressions and are the most recommended markers for the introgression of Yr15. These markers will facilitate simultaneous selection for multiple Yr genes and help to avoid escapees during the selection process. Engineering of improved chromosome 1BS that harbors multiple beneficial traits. We have implemented the knowledge and genetic resources accumulated in this project for the engineering of 1B "super-chromosome" that harbors multiple beneficial traits. We completed the generation of a chromosome including the rye 1RS distal segment associated with improved drought tolerance with the Yr gene, Yr15, and the strong gluten allele 7Bx-over-expressor (7Bxᴼᴱ). We have completed the introgression of this improved chromosome into our recently released variety Patwin-515HP and our rain fed variety Kern, as well as to our top breeding lines UC1767 and UC1745. Elucidating the mechanism of resistance exhibited by Yr36 (WKS1). The WHEAT KINASE START1 (WKS1) resistance gene (Yr36) confers partial resistance to Pst. We have shown that wheat plants transformed with WKS1 transcript are resistant to Pst. WKS1 is targeted to the chloroplast where it phosphorylates the thylakoid-associatedascorbateperoxidase (tAPX) and reduces its ability to detoxify peroxides. Based on these results, we propose that the phosphorylation of tAPX by WKS1 reduces the ability of the cells to detoxify ROS and contributes to cell death. Distribution and diversity of WKS in wild emmer populations. We have shown that WKS1 is present only in the southern distribution range of wild emmer in the Fertile Crescent. Sequence analysis revealed a high level of WKS1 conservation among wild emmer populations, in contrast to the high level of diversity observed in NB-LRR genes. This phenomenon shed some light on the evolution of genes that confer partial resistance to Pst. Three new WKS1 haplotypes displayed a resistance response, suggesting that they can be useful to improve wheat resistance to Pst. In summary, we have improved our understanding of cereals’ resistance mechanisms to rusts and we have used that knowledge to develop improved wheat varieties.
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Minz, Dror, Stefan J. Green, Noa Sela, Yitzhak Hadar, Janet Jansson, and Steven Lindow. Soil and rhizosphere microbiome response to treated waste water irrigation. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598153.bard.

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Abstract:
Research objectives : Identify genetic potential and community structure of soil and rhizosphere microbial community structure as affected by treated wastewater (TWW) irrigation. This objective was achieved through the examination soil and rhizosphere microbial communities of plants irrigated with fresh water (FW) and TWW. Genomic DNA extracted from soil and rhizosphere samples (Minz laboratory) was processed for DNA-based shotgun metagenome sequencing (Green laboratory). High-throughput bioinformatics was performed to compare both taxonomic and functional gene (and pathway) differences between sample types (treatment and location). Identify metabolic pathways induced or repressed by TWW irrigation. To accomplish this objective, shotgun metatranscriptome (RNA-based) sequencing was performed. Expressed genes and pathways were compared to identify significantly differentially expressed features between rhizosphere communities of plants irrigated with FW and TWW. Identify microbial gene functions and pathways affected by TWW irrigation*. To accomplish this objective, we will perform a metaproteome comparison between rhizosphere communities of plants irrigated with FW and TWW and selected soil microbial activities. Integration and evaluation of microbial community function in relation to its structure and genetic potential, and to infer the in situ physiology and function of microbial communities in soil and rhizospere under FW and TWW irrigation regimes. This objective is ongoing due to the need for extensive bioinformatics analysis. As a result of the capabilities of the new PI, we have also been characterizing the transcriptome of the plant roots as affected by the TWW irrigation and comparing the function of the plants to that of the microbiome. *This original objective was not achieved in the course of this study due to technical issues, especially the need to replace the American PIs during the project. However, the fact we were able to analyze more than one plant system as a result of the abilities of the new American PI strengthened the power of the conclusions derived from studies for the 1ˢᵗ and 2ⁿᵈ objectives. Background: As the world population grows, more urban waste is discharged to the environment, and fresh water sources are being polluted. Developing and industrial countries are increasing the use of wastewater and treated wastewater (TWW) for agriculture practice, thus turning the waste product into a valuable resource. Wastewater supplies a year- round reliable source of nutrient-rich water. Despite continuing enhancements in TWW quality, TWW irrigation can still result in unexplained and undesirable effects on crops. In part, these undesirable effects may be attributed to, among other factors, to the effects of TWW on the plant microbiome. Previous studies, including our own, have presented the TWW effect on soil microbial activity and community composition. To the best of our knowledge, however, no comprehensive study yet has been conducted on the microbial population associated BARD Report - Project 4662 Page 2 of 16 BARD Report - Project 4662 Page 3 of 16 with plant roots irrigated with TWW – a critical information gap. In this work, we characterize the effect of TWW irrigation on root-associated microbial community structure and function by using the most innovative tools available in analyzing bacterial community- a combination of microbial marker gene amplicon sequencing, microbial shotunmetagenomics (DNA-based total community and gene content characterization), microbial metatranscriptomics (RNA-based total community and gene content characterization), and plant host transcriptome response. At the core of this research, a mesocosm experiment was conducted to study and characterize the effect of TWW irrigation on tomato and lettuce plants. A focus of this study was on the plant roots, their associated microbial communities, and on the functional activities of plant root-associated microbial communities. We have found that TWW irrigation changes both the soil and root microbial community composition, and that the shift in the plant root microbiome associated with different irrigation was as significant as the changes caused by the plant host or soil type. The change in microbial community structure was accompanied by changes in the microbial community-wide functional potential (i.e., gene content of the entire microbial community, as determined through shotgun metagenome sequencing). The relative abundance of many genes was significantly different in TWW irrigated root microbiome relative to FW-irrigated root microbial communities. For example, the relative abundance of genes encoding for transporters increased in TWW-irrigated roots increased relative to FW-irrigated roots. Similarly, the relative abundance of genes linked to potassium efflux, respiratory systems and nitrogen metabolism were elevated in TWW irrigated roots when compared to FW-irrigated roots. The increased relative abundance of denitrifying genes in TWW systems relative FW systems, suggests that TWW-irrigated roots are more anaerobic compare to FW irrigated root. These gene functional data are consistent with geochemical measurements made from these systems. Specifically, the TWW irrigated soils had higher pH, total organic compound (TOC), sodium, potassium and electric conductivity values in comparison to FW soils. Thus, the root microbiome genetic functional potential can be correlated with pH, TOC and EC values and these factors must take part in the shaping the root microbiome. The expressed functions, as found by the metatranscriptome analysis, revealed many genes that increase in TWW-irrigated plant root microbial population relative to those in the FW-irrigated plants. The most substantial (and significant) were sodium-proton antiporters and Na(+)-translocatingNADH-quinoneoxidoreductase (NQR). The latter protein uses the cell respiratory machinery to harness redox force and convert the energy for efflux of sodium. As the roots and their microbiomes are exposed to the same environmental conditions, it was previously hypothesized that understanding the soil and rhizospheremicrobiome response will shed light on natural processes in these niches. This study demonstrate how newly available tools can better define complex processes and their downstream consequences, such as irrigation with water from different qualities, and to identify primary cues sensed by the plant host irrigated with TWW. From an agricultural perspective, many common practices are complicated processes with many ‘moving parts’, and are hard to characterize and predict. Multiple edaphic and microbial factors are involved, and these can react to many environmental cues. These complex systems are in turn affected by plant growth and exudation, and associated features such as irrigation, fertilization and use of pesticides. However, the combination of shotgun metagenomics, microbial shotgun metatranscriptomics, plant transcriptomics, and physical measurement of soil characteristics provides a mechanism for integrating data from highly complex agricultural systems to eventually provide for plant physiological response prediction and monitoring. BARD Report
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