Hungarian BAM files
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB20905 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| ERR1987192.fastq.gz | Fastqsanger.gz | |||
| ERR1987193.fastq.gz | Fastqsanger.gz | |||
| ERR1987194.fastq.gz | Fastqsanger.gz | |||
| ERR1987195.fastq.gz | Fastqsanger.gz |
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Project description:Abstract: During Drosophila oogenesis, germline stem cell (GSC) identity is maintained largely by preventing the expression of factors that promote differentiation. This is accomplished via the activity of several genes acting either in the GSC or its niche. The translational repressors, Nanos and Pumilio, act in GSCs to prevent differentiation, likely by inhibiting translation of early differentiation factors, while niche signals prevent differentiation by silencing transcription of the differentiation factor Bam. We have found that the DNA-associated protein Stonewall (Stwl) is also required for GSC maintenance. stwl is required cell-autonomously; clones of stwl- germ cells were lost by differentiation, and ectopic Stwl caused an expansion of GSCs. stwl mutants acted as Suppressors of Variegation, indicating stwl normally acts in chromatin-dependent gene repression. In contrast to several previously described GSC maintenance factors, Stwl likely functions epigenetically to prevent GSC differentiation. Stwl-dependent transcriptional repression does not target bam, but rather Stwl represses the expression of many genes, including those that may be targeted by Nanos/Pumilio translational inhibition. Experiment Overall Design: Genetic variation comparison between germ cells of Drosophila ovaries from bam mutant and stwl bam double mutant females. Six total samples were analyzed, with each genotype performed in triplicates.
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Project description:We present HDX-MS data of the BAM:SurA complex to define the interaction sites for SurA on BAM and vice versa.
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Project description:SummaryDespite the improvement in variant detection algorithms, visual inspection of the read-level data remains an essential step for accurate identification of variants in genome analysis. We developed BamSnap, an efficient BAM file viewer utilizing a graphics library and BAM indexing. In contrast to existing viewers, BamSnap can generate high-quality snapshots rapidly, with customized tracks and layout. As an example, we produced read-level images at 1000 genomic loci for >2500 whole-genomes.Availability and implementationBamSnap is freely available at https://github.com/parklab/bamsnap.Supplementary informationSupplementary data are available at Bioinformatics online.
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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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