BOL: Related items

SISRS: Site Identification from Short Read Sequences

Abhimanyu Singh — Wed, 28 Nov 2018 08:56:03 -0600

Next-gen sequence data such as Illumina HiSeq reads. Data must be sorted into folders by taxon (e.g. species or genus). Paired reads in fastq format must be specified by _R1 and _R2 in the (otherwise identical) filenames. Paired and unpaired reads must have a fastq file extension.

Address of the bookmark: https://github.com/rachelss/SISRS

KAST

Neel — Wed, 23 Feb 2022 08:28:36 -0600

Perform Alignment-free k-tuple frequency comparisons from sequences. This can be in the form of two input files (e.g. a reference and a query) or a single file for pairwise comparisons to be made.

Address of the bookmark: https://github.com/martinjvickers/KAST

KAST: Perform Alignment-free k-tuple frequency comparisons from sequences

Jit — Thu, 20 Sep 2018 08:56:35 -0500

Perform Alignment-free k-tuple frequency comparisons from sequences. This can be in the form of two input files (e.g. a reference and a query) or a single file for pairwise comparisons to be made.

Address of the bookmark: https://github.com/martinjvickers/KAST

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

Curated set of ribosomal RNA (rRNA) reference sequences (targeted loci) with verifiable organism

Rahul Nayak — Sun, 23 Feb 2020 02:17:30 -0600

MCBI have a curated set of ribosomal RNA (rRNA) reference sequences (targeted loci) with verifiable organism sources and current names. This set is critical for correctly identifying and classifying prokaryotic (bacteria and archaea) and fungal samples. To provide easy access to these sequences, we recently added a separate rRNA/ITS databases section on the nucleotide BLAST page for these targeted sequences that makes it convenient to quickly identify source organisms. The new databases are:

*16S ribosomal RNA (Bacteria and Archaea)

*18S ribosomal RNA sequences (SSU) from Fungi type and reference material

*28S ribosomal RNA sequences (LSU) from Fungi type and reference material

*Internal transcribed spacer region (ITS) from Fungi type and reference material

You can also download these from the BLAST db FTP area. See the NCBI Insights post for more detail.

Useful links

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BLAST form with rRNA/ITS databases

BLAST db download

Targeted loci

If you have any questions or concerns, please contact blast-help@ncbi.nlm.nih.gov

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

A simple tutorial for a complex ComplexHeatmap

Neel — Fri, 02 Apr 2021 06:18:32 -0500

ComplexHeatmap (Gu, Eils, and Schlesner (2016)) is an R Programming Language (R Core Team (2020)) package that is currently listed in the Bioconductor package repository.

install and load required packages

  require(RColorBrewer)
  require(ComplexHeatmap)
  require(circlize)
  require(digest)
  require(cluster)

If all load successfully, proceed to Part 3. Otherwise, go through the following code chunks in order to ensure that each package is installed and loaded properly.

BiocManager (Morgan (2019))

Address of the bookmark: https://github.com/kevinblighe/E-MTAB-6141

Recombination detection tool

Jit — Tue, 02 Feb 2016 10:11:14 -0600

A program to detect recombination hotspots using population genetic data.

More at https://github.com/auton1/LDhot

Address of the bookmark: https://github.com/auton1/LDhot

RASTtk : algorithm for building custom annotation pipelines and annotating batches of genomes

Abhi — Wed, 27 Apr 2016 11:07:59 -0500

The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.

More at http://www.nature.com/articles/srep08365

Address of the bookmark: http://rast.nmpdr.org/

MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping

Neel — Fri, 20 May 2016 18:53:49 -0500

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery.

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581