BOL: Related items

MIX: Combining multiple assemblies from NGS data

Rahul Nayak — Tue, 08 May 2018 04:58:05 -0500

Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the cumulative contig length.

The Mix algorithm, approach and results were published in BMC bioinformatics : http://www.biomedcentral.com/1471-2105/14/S15/S16.

Address of the bookmark: https://github.com/cbib/MIX

Cactus: a reference-free whole-genome multiple alignment program

Jit — Mon, 12 Aug 2019 07:52:33 -0500

Cactus is a reference-free whole-genome multiple alignment program. The principal algorithms are described here: https://doi.org/10.1101/gr.123356.111

Cactus uses substantial resources. For primate-sized genomes (3 gigabases each), you should expect Cactus to use approximately 120 CPU-days of compute per genome, with about 120 GB of RAM used at peak. The requirements scale roughly quadratically, so aligning two 1-megabase bacterial genomes takes only 1.5 CPU-hours and 14 GB RAM.

Address of the bookmark: https://github.com/ComparativeGenomicsToolkit/cactus

destruct

Jitendra Prajapati — Mon, 29 Feb 2016 17:34:59 -0600

Destruct is a tool for joint prediction of rearrangement breakpoints from single or multiple tumour samples.

More at https://bitbucket.org/dranew/destruct

Address of the bookmark: https://bitbucket.org/dranew/destruct

SLIDESORT-BPR

Abhimanyu Singh — Mon, 20 Nov 2017 09:19:52 -0600

Chromosomal rearrangement events are caused by abnormal breaking and rejoining of DNA molecules. They are responsible for many of the cancer related diseases. Detecting the DNA breaking and repairing mechanism, therefore, may offer vital clues about the pathologic causes and diagnostic/therapeutic target of these diseases. But this effort also poses considerable challenges, because the structural variations and the genomes are different from one person to another. Intermediate comparison via reference genome could lead to the loss information. Unlike the current methods which make use the reference genome, we developed a method to detect the breakpoint reads directly from observing the differences between two (or more) NGS short reads samples. Slidesort-BPR is a command line tool implemented in C++.

Address of the bookmark: https://github.com/ewijaya/slidesort-bpr

LAST

Archana Malhotra — Wed, 09 Mar 2016 14:27:01 -0600

LAST can:

Handle big sequence data, e.g:
- Compare two vertebrate genomes
- Align billions of DNA reads to a genome
Indicate the reliability of each aligned column.
Use sequence quality data properly.
Compare DNA to proteins, with frameshifts.
Compare PSSMs to sequences
Calculate the likelihood of chance similarities between random sequences.
Do split and spliced alignment.
Train alignment parameters for unusual kinds of sequence (e.g. nanopore).

Address of the bookmark: http://last.cbrc.jp/

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: 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

DECIPHER; a software toolset for deciphering and managing biological sequences efficiently using the R

BioStar — Sun, 09 Dec 2018 19:06:17 -0600

DECIPHER is a software toolset that can be used for deciphering and managing biological sequences efficiently using the R programming language. The R package is distributed as platform independent source code under the GPL version 3 license. Some functionality of the program is accessible online through web tools.

Address of the bookmark: http://www2.decipher.codes/

Miropeats: discovers regions of sequence similarity amongst any set of DNA sequences

Poonam Mahapatra — Mon, 26 Aug 2019 17:55:24 -0500

Miropeats discovers regions of sequence similarity amongst any set of DNA sequences and then presents this similarity information graphically. Sequence similarity searching is a very general tool that forms the basis of many different biological sequence analyses but it is limited by the verbosity of traditional alignment presentation styles. Miropeats enhances the utility of conventional DNA sequence comparisons when looking at long lengths of sequence similarity by summarizing extensive large scale sequence similarities on a single page of graphics. The latest version of Miropeats can be used as a general pairwise alignment program or in its traditional role sorting out a big mess of overlapping or similar regions.

Address of the bookmark: http://www.littlest.co.uk/software/bioinf/old_packages/miropeats/

kebabs: package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods

Rahul Nayak — Fri, 04 Mar 2022 00:14:11 -0600

The kebabs package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods. Biological sequences include DNA, RNA, and amino acid (AA) sequences. Sequence kernels define similarity measures between sequences. The package implements some of the most important kernels for sequence analysis in a very flexible and efficient way and extends the standard position-independent functionality of these kernels in a novel way to take the position of patterns in the sequences into account for the similarity measure.

http://www.bioinf.jku.at/software/kebabs/

http://bioconductor.org/packages/release/bioc/vignettes/kebabs/inst/doc/kebabs.pdf

Address of the bookmark: http://www.bioinf.jku.at/software/kebabs/