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ABSTRACT: 10k Salmonella Project

PROVIDER: PRJEB35182 | ENA |

REPOSITORIES: ENA

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Dataset's files

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			Action	DRS
	ERR3633040_1.fastq.gz	Fastqsanger.gz
	ERR3633040_2.fastq.gz	Fastqsanger.gz
	ERR3633050_1.fastq.gz	Fastqsanger.gz
	ERR3633050_2.fastq.gz	Fastqsanger.gz
	ERR3633060_1.fastq.gz	Fastqsanger.gz

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Project description:10k Salmonella Project

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biobambam: tools for read pair collation based algorithms on BAM files

Project description:Background Sequence alignment data is often ordered by coordinate (id of the reference sequence plus position on the sequence where the fragment was mapped) when stored in BAM files, as this simplifies the extraction of variants between the mapped data and the reference or of variants within the mapped data. In this order paired reads are usually separated in the file, which complicates some other applications like duplicate marking or conversion to the FastQ format which require to access the full information of the pairs. Results In this paper we introduce biobambam, a set of tools based on the efficient collation of alignments in BAM files by read name. The employed collation algorithm avoids time and space consuming sorting of alignments by read name where this is possible without using more than a specified amount of main memory. Using this algorithm tasks like duplicate marking in BAM files and conversion of BAM files to the FastQ format can be performed very efficiently with limited resources. We also make the collation algorithm available in the form of an API for other projects. This API is part of the libmaus package. Conclusions In comparison with previous approaches to problems involving the collation of alignments by read name like the BAM to FastQ or duplication marking utilities our approach can often perform an equivalent task more efficiently in terms of the required main memory and run-time. Our BAM to FastQ conversion is faster than all widely known alternatives including Picard and bamUtil. Our duplicate marking is about as fast as the closest competitor bamUtil for small data sets and faster than all known alternatives on large and complex data sets.

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Transformation of alignment files improves performance of variant callers for long-read RNA sequencing data.

Project description:Long-read RNA sequencing (lrRNA-seq) produces detailed information about full-length transcripts, including novel and sample-specific isoforms. Furthermore, there is an opportunity to call variants directly from lrRNA-seq data. However, most state-of-the-art variant callers have been developed for genomic DNA. Here, there are two objectives: first, we perform a mini-benchmark on GATK, DeepVariant, Clair3, and NanoCaller primarily on PacBio Iso-Seq, data, but also on Nanopore and Illumina RNA-seq data; second, we propose a pipeline to process spliced-alignment files, making them suitable for variant calling with DNA-based callers. With such manipulations, high calling performance can be achieved using DeepVariant on Iso-seq data.

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Genome-wide SNP Microarray Mapping in Basal Cell Carcinomas Unveils Uniparental Disomy as a Key Somatic Event.

Project description:Affymetrix 10K SNP mapping arrays were used to profile 14 basal cell carcinomas (BCCs) with matched blood DNA samples. Loss of heterozygosity (LOH) and copy number abnormality (CNA) profiles were derived from each tumour-blood pair. Keywords: Genomic DNA on Affymetrix 10K SNP array

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Project description:10K Asian Rice (Oryza Sativa) Genome Project (10KRGP).

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Short-read fastA files dataset from complexity-reduced genotyping by sequencing data of bacterial isolates from a public hospital in Australia.

Project description:This data article contains short-read sequences (length 30-69 bp) obtained from complexity-reduced genotyping by sequencing (GBS) of 165 samples bacterial isolates from hospital patients in the Australian Capital Territory, between 2013 and 2015. These samples represented 14 bacterial species. Data format is shown as filtered fastA files obtained from an Illumina HiSeq2500 sequencer. The experimental factors of this research used three complexity reduction methods with three combinations of restriction enzymes: PstI with MseI, PstI with HpaII and MseI with HpaII.

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Covid19 WGS Raw Read files

Project description:The Samples were collected from Saint James's Hospital, Dublin and after informed consent Extracted Genomic DNA samples were prepared and sequenced using the Automated Illumina TruSeq PCR-free methodology.

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