Herald:The Biostar Herald for Tuesday, October 26, 2021
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The Biostar Herald publishes user submitted links of bioinformatics relevance. It aims to provide a summary of interesting and relevant information you may have missed. You too can submit links here.

This edition of the Herald was brought to you by contribution from GenoMax, Istvan Albert, lethalfang, and was edited by GenoMax, Istvan Albert,


Pangenome Graphs (www.ncbi.nlm.nih.gov)

Pangenome graphs stand to become a ubiquitous tool in genomics. Although it is unclear if they will replace linear reference genomes, their ability to harmoniously relate multiple sequence and coordinate systems will make them useful irrespective of which pangenomic models become most common in the future

submitted by: Istvan Albert


Home | Modern Statistics for Modern Biology (www.huber.embl.de)

The aim of this book is to enable scientists working in biological research to quickly learn many of the important ideas and methods that they need to make the best of their experiments and of other available data.

submitted by: Istvan Albert


Home | gnomAD news (gnomad.broadinstitute.org)

The Genome Aggregation Database (gnomAD), is a coalition of investigators seeking to aggregate and harmonize exome and genome sequencing data from a variety of large-scale sequencing projects, and to make summary data available for the wider scientific community

submitted by: Istvan Albert


submitted by: Istvan Albert


SAMtools/BCFtools/HTSlib - Downloads (www.htslib.org)

Samtools v.1.14 released.

submitted by: GenoMax


Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing | Nature Biotechnology (doi.org)

Establishing reference samples, data, and call sets for somatic mutation detection

The FDA-led MAQC-IV/SEQC2 Consortium has published numerous whole-genome and whole-exome sequencing replicates from multiple sequencing centers using multiple and library preps for a pair of tumor-normal reference samples, along with the high-confidence somatic mutation reference call set. The immediate use case for these resources including training for machine learning models (e.g., Sahraeian S.M.E. _et al_. bioRxiv 2019) and evaluating bioinformatic algorithms and pipelines (e.g., Xiao W. _et al_. Nat Biotechnol 2021 or SharedIt Link). The methods to establish the high-confidence somatic mutation benchmark call set is described in Fang LT, _et al_. Nat Biotechnol 2021 (or SharedIt Link).

We also have a video presentation describing the results.

submitted by: lethalfang


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