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This edition of the Herald was brought to you by contribution from Istvan Albert, aswathyseb, Shred, and was edited by Istvan Albert,
Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates | Genome Biology | Full Text (genomebiology.biomedcentral.com)
RNA velocity analysis of single cells offers the potential to predict temporal dynamics from gene expression. In many systems, RNA velocity has been observed to produce a vector field that qualitatively reflects known features of the system. However, the limitations of RNA velocity estimates are still not well understood.
submitted by: Istvan Albert
https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkad903/7331009
GenBank® (https://www.ncbi.nlm.nih.gov/genbank/) is a comprehensive, public database that contains 25 trillion base pairs from over 3.7 billion nucleotide sequences for 557 000 formally described species. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. Recent updates include policies for including spatio-temporal metadata, clarified documentation for GenBank data processing, enhanced foreign contamination screening tools, new processes in the Submission Portal, migration of Entrez Genome and Assembly displays into NCBI Datasets, and the impending retirement of tbl2asn, replaced by table2asn.
submitted by: Istvan Albert
Release of DeepVariant v1.6.
Support for haploid regions, chrX/Y.
Workflow for Pangenome FASTQ-to-VCF.
Major DeepTrio improvements for de novo variants.
Models for CompleteGenomics T7, G400
Add NovaSeqX to training data
Release by @kishwarshafin https://t.co/ShaxqDNTfX
— Andrew Carroll (@acarroll_ATG) October 26, 2023
Release of DeepVariant v1.6.
Support for haploid regions, chrX/Y.
Workflow for Pangenome FASTQ-to-VCF.
Major DeepTrio improvements for de novo variants.
Models for CompleteGenomics T7, G400
Add NovaSeqX to training data
Release by @kishwarshafin https://t.co/ShaxqDNTfX
submitted by: Istvan Albert
GitHub - google/deepsomatic: DeepSomatic is an analysis pipeline that uses a deep neural network to call somatic variants from tumor-normal sequencing data. (github.com)
Google released Deepsomatic, a porting of the already well known DeepVariant, to call somatic variants from WGS from both Illumina and Pacbio, using paired tumor-normal samples. One of the developers stated they're not planning to develop a WES model [source: https://github.com/google/deepsomatic/issues/4#issuecomment-1779657480 ] To date (version 1.6) no code released through Github.
submitted by: Shred
The simpleaf (simple-AF) paper by @DongzeHe & me was recently published in @OUPBioinfo https://t.co/8pEwSHeXoU. It describes the simpleaf tool, written in #rustlang, designed to track best practices & make processing #SingleCell #scrna data with alevin-fry even easier. 1/x
— 𝕐 (@rob@genomic.social) (@nomad421) October 25, 2023
The simpleaf (simple-AF) paper by @DongzeHe & me was recently published in @OUPBioinfo https://t.co/8pEwSHeXoU. It describes the simpleaf tool, written in #rustlang, designed to track best practices & make processing #SingleCell #scrna data with alevin-fry even easier. 1/x
— 𝕐 (@rob@genomic.social) (@nomad421) October 25, 2023submitted by: Istvan Albert
Initial release of DeepSomatic, which identifies subclonal variants when given tumor and normal BAM files. Pre-trained models and case studies available for Illumina and PacBio. Development led by @kishwarshafin which built off a framework by @pichuanhttps://t.co/YpB46RlhiB
— Andrew Carroll (@acarroll_ATG) October 24, 2023
Initial release of DeepSomatic, which identifies subclonal variants when given tumor and normal BAM files. Pre-trained models and case studies available for Illumina and PacBio. Development led by @kishwarshafin which built off a framework by @pichuanhttps://t.co/YpB46RlhiB
— Andrew Carroll (@acarroll_ATG) October 24, 2023submitted by: Istvan Albert
Identifying significantly impacted pathways: a comprehensive review and assessment | Genome Biology | Full Text (genomebiology.biomedcentral.com)
Many high-throughput experiments compare two phenotypes such as disease vs. healthy, with the goal of understanding the underlying biological phenomena characterizing the given phenotype. Because of the importance of this type of analysis, more than 70 pathway analysis methods have been proposed so far. These can be categorized into two main categories: non-topology-based (non-TB) and topology-based (TB). Although some review papers discuss this topic from different aspects, there is no systematic, large-scale assessment of such methods. Furthermore, the majority of the pathway analysis approaches rely on the assumption of uniformity of p values under the null hypothesis, which is often not true.
submitted by: aswathyseb
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