BOL: Related items

SMASH: An alignment-free tool to find and visualise rearrangements between pairs of DNA sequences

Jit — Thu, 21 Dec 2017 08:26:57 -0600

SMASH is a completely alignment-free method to find and visualise rearrangements between pairs of DNA sequences. The detection is based on relative compression, namely using a FCM, also known as Markov model, of high context order (typically 20). The method has been approached with a tool (also called SMASH). For visualization, SMASH outputs a SVG image, with an ideogram output architecture, where the patterns are represented with several HSV values (only value varies). The following image, illustrating the information maps between human and chimpanzee for the several chromosomes, depicts an example:

Address of the bookmark: https://github.com/pratas/smash

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

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/

UPhO: Scripts for homology and orthology assessment from genomic sequences.

BioStar — Mon, 14 Jan 2019 10:36:42 -0600

UPhO finds orthologs with and without inparalogs from input gene family trees. Refer to the Documentation.pdf for more detailed explanations on its usage, installation and dependencies. Type UPhO.py -h for help.

The only input requierement for UPhO is a tree (or trees) in Newick format in which the leaves are named with a species idenfifier, a field separator, and sequence identifier. By default, the field separator is the character "|" but custom delimiters can be defined. Examples of trees to test UPhO are provided in the TestData folder.

Address of the bookmark: https://github.com/ballesterus/UPhO

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

NucDiff: In-depth characterization and annotation of differences between two sets of DNA sequences

Jit — Tue, 05 May 2020 10:35:48 -0500

NucDiff locates and categorizes differences between two closely related nucleotide sequences. It is able to deal with very fragmented genomes, structural rearrangements and various local differences. These features make NucDiff to be perfectly suitable to compare assemblies with each other or with available reference genomes.

NucDiff provides information about the types of differences and their locations. It is possible to upload the results into genome browser for visualization and further inspection. It was written in Python and uses the NUCmer package from MUMmer[1] for sequence comparison.

Address of the bookmark: https://github.com/uio-cels/NucDiff

Common methods to discover tandem repeats

BioStar — Thu, 09 Mar 2023 02:40:52 -0600

Tandem repeats are DNA sequences that are repeated in a contiguous manner in the genome. These sequences are often used as genetic markers and are important in many areas of genetics and genomics research. Here are some methods for discovering tandem repeats in genomes:

Tandem Repeat Finder: Tandem Repeat Finder is a software tool that identifies tandem repeats in DNA sequences. It is available for free download and can be used on both nucleotide and protein sequences. The tool uses a statistical algorithm to identify repeats based on their length, copy number, and overall composition.
RepeatMasker: RepeatMasker is another software tool that can identify tandem repeats in DNA sequences. It works by comparing the input sequence to a database of known repeats and then identifies any tandem repeats that match those in the database.
PCR-based methods: Polymerase chain reaction (PCR) can be used to amplify and detect tandem repeats in genomic DNA. PCR primers are designed to flank the tandem repeat region, and amplification of the target DNA fragment can be visualized on a gel. This method can be useful for detecting novel tandem repeats and for genotyping.
Southern blotting: Southern blotting is a classic method for detecting DNA fragments in a sample. It can be used to detect tandem repeats by digesting genomic DNA with a restriction enzyme, separating the fragments by gel electrophoresis, and then probing the blot with a tandem repeat-specific probe.

Overall, a combination of these methods can be used to comprehensively identify tandem repeats in genomes.

SVbyEye: R Package to visualize alignments between two or multiple DNA sequences

LEGE — Tue, 17 Sep 2024 02:34:57 -0500

R Package to visualize alignments between two or multiple DNA sequences including
a number of functionalities to facilitate processing of alignments in PAF format.

SVbyEye, an open-source R package to visualize and annotate sequence-to-sequence alignments along with various functionalities to process alignments in PAF format. The tool facilitates the characterization of complex SVs in the context of sequence homology helping resolve the mechanisms underlying their formation. Availability and implementation SVbyEye is available at https://github.com/daewoooo/SVbyEye.

Author: David Porubsky

Address of the bookmark: https://github.com/daewoooo/SVbyEye

BLAST+ 2.10.0 released

Jit — Wed, 18 Dec 2019 20:44:11 -0600

The BLAST+ 2.10.0 release is now available from at FTP site. The new version offers the following improvements:

updated composition-based statistics for protein-protein (including translated BLAST) comparisons to provide stable results when you request fewer than the default number of results
an experimental Adaptive Composition Based Statistics option that increases the likelihood of finding novel results. To enable this option set the environment variable ADAPTIVE_CBS to 1. We welcome your feedback on this new option.

See the release notes for details on more improvements and bug fixes with this release.

BLAST+ is also available in docker, please read more for details.

The new version fully supports the version 5 (v5) databases with built in taxonomy and other improvements. For more information on v5 databases (download), see the previous NCBI Insights article and the recording of our webinar. If you are still using the older version 4 (v4) databases, we recommend you begin using the v5 version as soon as possible. We will discontinue updates to the older v4 databases in early 2020.

BLAST+ Team

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/