Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing.
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ABSTRACT:
SUBMITTER: Yassour M
PROVIDER: S-EPMC2638735 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The improvements in high throughput sequencing technologies (HTS) made clinical sequencing projects such as ClinSeq and Genomics England feasible. Although there are significant improvements in accuracy and reproducibility of HTS based analyses, the usability of these types of data for diagnostic and prognostic applications necessitates a near perfect data generation. To assess the usability of a widely used HTS platform for accurate and reproducible clinical applications in terms of robustness, we generated whole genome shotgun (WGS) sequence data from the genomes of two human individuals in two different genome sequencing centers. After analyzing the data to characterize SNPs and indels using the same tools (BWA, SAMtools, and GATK), we observed significant number of discrepancies in the call sets. As expected, the most of the disagreements between the call sets were found within genomic regions containing common repeats and segmental duplications, albeit only a small fraction of the discordant variants were within the exons and other functionally relevant regions such as promoters. We conclude that although HTS platforms are sufficiently powerful for providing data for first-pass clinical tests, the variant predictions still need to be confirmed using orthogonal methods before using in clinical applications.
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Project description:BACKGROUND: Massively parallel sequencing allows for genome-wide hypothesis-free investigation of for instance transcription factor binding sites or histone modifications. Although nucleotide resolution detailed information can easily be generated, biological insight often requires a more general view of patterns (footprints) over distinct genomic features such as transcription start sites, exons or repetitive regions. The construction of these footprints is however a time consuming task. METHODS: The presented software generates a binary representation of the signals enabling fast and scalable lookup. This representation allows for footprint generation in mere minutes on a desktop computer. Several different input formats are accepted, e.g. the SAM format, bed-files and the UCSC wiggle track. CONCLUSIONS: Hypothesis-free investigation of genome wide interactions allows for biological data mining at a scale never before seen. Until recently, the main focus of analysis of sequencing data has been targeted on signal patterns around transcriptional start sites which are in manageable numbers. Today, focus is shifting to a wider perspective and numerous genomic features are being studied. To this end, we provide a system allowing for fast querying in the order of hundreds of thousands of features.
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Project description:Lettuce downy mildew (Bremia lactucae) is a rapidly adapting oomycete pathogen affecting commercial lettuce cultivation. Oomycetes are known to use a diverse arsenal of secreted proteins (effectors) to manipulate their hosts. Two classes of effector are known to be translocated by the host: the RXLRs and Crinklers. To gain insight into the repertoire of effectors used by B. lactucae to manipulate its host, we performed massively parallel sequencing of cDNA derived from B. lactucae spores and infected lettuce (Lactuca sativa) seedlings. From over 2.3 million 454 GS FLX reads, 59 618 contigs were assembled representing both plant and pathogen transcripts. Of these, 19 663 contigs were determined to be of B. lactucae origin as they matched pathogen genome sequences (SOLiD) that were obtained from >270 million reads of spore-derived genomic DNA. After correction of cDNA sequencing errors with SOLiD data, translation into protein models and filtering, 16 372 protein models remained, 1023 of which were predicted to be secreted. This secretome included elicitins, necrosis and ethylene-inducing peptide 1-like proteins, glucanase inhibitors and lectins, and was enriched in cysteine-rich proteins. Candidate host-translocated effectors included 78 protein models with RXLR effector features. In addition, we found indications for an unknown number of Crinkler-like sequences. Similarity clustering of secreted proteins revealed additional effector candidates. We provide a first look at the transcriptome of B. lactucae and its encoded effector arsenal.
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Project description:Single-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce dimensionality and achieve informative cell typing results. We present an ab initio method that performs unsupervised marker selection by identifying genes that have subpopulation-discriminative expression levels and are co- or mutually-exclusively expressed with other genes. Consistent improvements in cell-type classification and biologically meaningful marker selection are achieved by applying SCMarker on various datasets in multiple tissue types, followed by a variety of clustering algorithms. The source code of SCMarker is publicly available at https://github.com/KChen-lab/SCMarker.
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