BOL: All site bookmarks

GEnView: A phylogeny based comparative genomics software to analyze the genetic environment of genes

Abhi — Tue, 28 Dec 2021 01:49:03 -0600

A phylogeny based comparative genomics software to analyze the genetic environment of genes. The user can select one or several taxa and provide one or several reference protein(s). Genomes and plasmids (based on user choice) will be downloaded from the NCBI Assembly/NR database and searched for the respective gene. Alternatively, custom genomes can be provided. User selected stretches (20kbp by default) of the genes genetic environment are extracted, annotated and aligned between all genomes. The sequences are then visualized, enabling comparison of synteny and gene content.

More at https://pubmed.ncbi.nlm.nih.gov/34951622/

Address of the bookmark: https://github.com/EbmeyerSt/GEnView

A guide to machine learning for biologists

Abhi — Tue, 28 Dec 2021 01:43:25 -0600

Because of the increasing size and inherent complexity of biological data, there has been an increase in the application of machine learning in biology to create useful and predictive models of the underlying biological processes. All machine learning techniques fit models to data; nevertheless, the specific methods are highly variable and can appear baffling at first glance. In this Review, we hope to give readers a moderate introduction to a few fundamental machine learning techniques, including the most recently created and frequently used deep neural network techniques. We illustrate how different algorithms may be adapted to specific types of biological data, as well as some best practises and points to consider when embarking on machine learning studies. There is also discussion of several upcoming directions in machine learning methodology.

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0

VCF2PopTree: a client-side software to construct population phylogeny from genome-wide SNPs

Surabhi Chaudhary — Sat, 25 Dec 2021 00:13:31 -0600

VCF2PopTree is a client-side software written in Javascript and it runs purely within the user’s computer/browser. VCF2PopTree is compatible with all population browsers including Chrome, Opera, Edge and Firefox and works equally efficient in Mac, Windows and Linux (Ubuntu).

Furthermore, it displays the tree in a mobile phone (iPhone and Android) if the input file size is small. CITATION: Subramanian, S., Ramasamy, U. and Chen, D. (2019). VCF2PopTree: a client-side software to construct population phylogeny from genome-wide SNPs. Peer J. x:yy.

Address of the bookmark: https://github.com/sansubs/vcf2pop

vcf2maf convert !

Surabhi Chaudhary — Fri, 17 Dec 2021 03:20:01 -0600

To convert a VCF into a MAF, each variant must be mapped to only one of all possible gene transcripts/isoforms that it might affect. But even within a single isoform, a Missense_Mutation close enough to a Splice_Site, can be labeled as either in MAF format, but not as both. This selection of a single effect per variant, is often subjective. And that's what this project attempts to standardize. The vcf2maf and maf2maf scripts leave most of that responsibility to Ensembl's VEP, but allows you to override their "canonical" isoforms, or use a custom ExAC VCF for annotation. Though the most useful feature is the extensive support in parsing a wide range of crappy MAF-like or VCF-like formats we've seen out in the wild.

Address of the bookmark: https://github.com/mskcc/vcf2maf

maftools

Surabhi Chaudhary — Fri, 17 Dec 2021 03:18:28 -0600

With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widely accepted and used to store somatic variants detected. The Cancer Genome Atlas Project has sequenced over 30 different cancers with sample size of each cancer type being over 200. Resulting data consisting of somatic variants are stored in the form of Mutation Annotation Format. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner from either TCGA sources or any in-house studies as long as the data is in MAF format.

https://www.bioconductor.org/packages/devel/bioc/vignettes/maftools/inst/doc/maftools.html

Address of the bookmark: https://github.com/PoisonAlien/maftools

UniqueKmer: Generate unique KMERs for every contig in a FASTA file

Abhi — Fri, 17 Dec 2021 00:08:15 -0600

Generate unique k-mers for every contig in a FASTA file.

Unique k-mer is consisted of k-mer keys (i.e. ATCGATCCTTAAGG) that are only presented in one contig, but not presented in any other contigs (for both forward and reverse strands).

This tool accepts the input of a FASTA file consisting of many contigs, and extract unique k-mers for each contig.

The output unique k-mer file and Genome file can be used for fastv: https://github.com/OpenGene/fastv, which is an ultra-fast tool to identify and visualize microbial sequences from sequencing data.

https://github.com/OpenGene/UniqueKMER

Address of the bookmark: https://github.com/OpenGene/UniqueKMER

Peregrine & SHIMMER Genome Assembly Toolkit

Abhi — Thu, 16 Dec 2021 02:50:19 -0600

Peregrine is a fast genome assembler for accurate long reads (length > 10kb, accuracy > 99%). It can assemble a human genome from 30x reads within 20 cpu hours from reads to polished consensus. It uses Sparse HIereachical MimiMizER (SHIMMER) for fast read-to-read overlaping without quadratic comparisions used in other OLC assemblers.

Address of the bookmark: https://github.com/cschin/Peregrine

Rules for Pango Lineage !

Abhi — Tue, 14 Dec 2021 04:40:26 -0600

All the rules to classify a Lineage !

https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

Address of the bookmark: https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

Corona Virus Genome and Data Download !

Abhi — Sun, 12 Dec 2021 23:34:54 -0600

Genes and its related metadata could be found on https://www.ncbi.nlm.nih.gov/datasets/coronavirus/genomes/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/datasets/coronavirus/genomes/

Corona Virus Literature !

Abhi — Sun, 12 Dec 2021 23:30:56 -0600

LitCovid is a curated literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus. It is the most comprehensive resource on the subject, providing a central access to 201482 (and growing) relevant articles in PubMed. The articles are updated daily and are further categorized by different research topics (e.g. Long Covid) and geographic locations for improved access. You can learn more at Chen et al. Nature (2020) or our FAQ, and download our data here.

https://www.ncbi.nlm.nih.gov/research/coronavirus/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/research/coronavirus/