BOL: Related items

VG: variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods

Jit — Tue, 28 Jan 2020 03:53:24 -0600

Variation graphs provide a succinct encoding of the sequences of many genomes. A variation graph (in particular as implemented in vg) is composed of:

nodes, which are labeled by sequences and ids
edges, which connect two nodes via either of their respective ends
paths, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges

Address of the bookmark: https://github.com/vgteam/vg

Compressive Genomics

Rahul Agarwal — Sun, 11 Aug 2013 11:13:58 -0500

The key to finding a solution is to notice that most genomicsequences differ by very little. It may well be that the number of complete genome sequences being stored is increasing rapidly, but the actual amount of new data is very small. In other words, a single DNA sequence isn't particularly compressible but a set of sequences shares so much in common that the redundancy can be used to store them in a much smaller storage space. (Source:e-article from Alex Armstrong)

http://www.i-programmer.info/news/181-algorithms/4537-a-new-dna-sequence-search-compressive-genomics.html

http://en.wikipedia.org/wiki/Compression_of_Genomic_Re-Sequencing_Data

http://www.nature.com/nbt/journal/v30/n7/full/nbt.2241.html

http://bioinformatics.oxfordjournals.org/content/29/13/i283.full

http://groups.csail.mit.edu/cb/cast/

TEannot

Jit — Thu, 18 Aug 2016 10:02:03 -0500

We advise to run first the TEdenovo pipeline but it is not compulsory. We suppose you begin by running the TEannot pipeline on the example provided in the directory "db/" rather than directly on your own genomic sequences. Thus, from now on, the project name is "DmelChr4".

Address of the bookmark: https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto

Katju Lab

Fri, 26 Feb 2016 03:25:32 -0600

TheLab seek to understand the genetic factors contributing to genomic variation and phenotypic diversity. To this end, we employ molecular and bioinformatic tools to study evolutionary processes at the level of populations, both experimental and natural, and genomes. Our research interests encompass a wide range of topics, including the evolution of organellar and nuclear genomes, gene duplication and the origin of novel function, and the fitness and phenotypic consequences of mutation in evolution. For details regards ongoing projects, please see the Research page.

http://katjulab.com/research.html

gVolante: Completeness Assessment of Genome/Transcriptome Sequences

Neel — Sun, 13 Jan 2019 07:03:25 -0600

A brand-new web server, gVolante, which provides an online tool for (i) on-demand completeness assessment of sequence sets by means of the previously developed pipelines CEGMA and BUSCO and (ii) browsing pre-computed completeness scores for publicly available data in its database section

Address of the bookmark: https://gvolante.riken.jp/analysis.html

GenBank release 257.0 is now available!

Neel — Wed, 23 Aug 2023 00:23:23 -0500

GenBank release 257.0 is now available! This release has 25.10 trillion bases and 3.69 billion records. Learn more: https://ncbiinsights.ncbi.nlm.nih.gov/2023/08/21/genbank-release-257/

GenBank release 257.0 (8/15/2023) is now available on the NCBI FTP site. This release has 25.10 trillion bases and 3.69 billion records.

The current release has:

246,119,175 traditional records containing 2,112,058,517,945 base pairs of sequence data
2,631,493,489 WGS records containing 22,294,446,104,543 base pairs of sequence data
686,271,945 bulk-oriented TSA records containing 646,176,166,908 base pairs of sequence data
124,421,006 bulk-oriented TLS records containing 48,289,699,026 base pairs of sequence data

S-plot2: creates an interactive, two-dimensional heatmap of sequences

Jit — Fri, 28 Sep 2018 05:36:19 -0500

S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.

http://www.putonti-lab.com/uploads/4/5/3/0/45307835/s-plot2_tutorial.pdf

http://journals.sagepub.com/doi/pdf/10.1177/1176934318797354

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot

SiLiX: implements an ultra-efficient algorithm for the clustering of homologous sequences

Jit — Wed, 12 Dec 2018 09:22:41 -0600

The software package SiLiX implements an ultra-efficient algorithm for the clustering of homologous sequences, based on single transitive links (single linkage) with alignment coverage constraints.

SiLiX adopts a graph-theoretical framework to interpret similarity pairs as edges of a network. A very efficient algorithm, based on the Disjoint Sets Data Structure, allows the computation of sequence families with low time and space requirements.

A parallel version of SiLiX, based on MPI, is also available in this package and has been proved to be scalable, so that its allows the study of very large datasets.

SiLiX is already included in the analysis pipeline for HOGENOM.

Address of the bookmark: http://lbbe.univ-lyon1.fr/SiLiX?lang=fr

Kalign: fast multiple sequence alignment program for biological sequences.

BioStar — Fri, 01 Nov 2019 00:20:41 -0500

Kalign is a fast multiple sequence alignment program for biological sequences.

Align sequences and output the alignment in MSF format:

kalign -i BB11001.tfa -f msf  -o out.msf

Align sequences and output the alignment in clustal format:

kalign -i BB11001.tfa -f clu -o out.clu

Re-align sequences in an existing alignment:

kalign -i BB11001.msf  -o out.afa

Reformat existing alignment:

kalign -i BB11001.msf -r afa -o out.afa

Address of the bookmark: https://github.com/TimoLassmann/kalign

Tiara: deep learning-based classification system for eukaryotic sequences

Rahul Nayak — Mon, 14 Mar 2022 23:02:11 -0500

With a large number of metagenomic datasets becoming available, eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step toward a better understanding of eukaryotic diversity.

Address of the bookmark: https://academic.oup.com/bioinformatics/article/38/2/344/6375939