BOL: Related items

OrthoANI: An improved algorithm and software for calculating average nucleotide identity

Abhimanyu Singh — Wed, 12 Dec 2018 08:36:08 -0600

OAT uses OrthoANI to measure the overall similarity between two genome sequences. ANI and OrthoANI are comparable algorithms: they share the same species demarcation cut-off at 95~96% and large comparison studies have demonstrated both algorithms to produce near identical reciprocal similarities. Details of the OrthoANI algorithm is given in (Lee et al. 2015). OAT employs an easy-to-follow Graphical User Interface that allow researchers to calculate OrthoANI values between genomes of interest without unfamiliar Command Line Environments. Moreover, the OAT_cmd command-line software can be integrated into preexisting bioinformatics pipelines.

Address of the bookmark: https://www.ezbiocloud.net/tools/orthoani

Genome Stability Laboratory

Mon, 07 Mar 2016 04:16:32 -0600

The bakers yeast, Saccharomyces cerevisiae is an ideal model organism to understand mechanisms of meiotic chromosome segregation. In S. cerevisiae and in mammals, the majority of meiotic crossovers are formed through a highly conserved MSH4p-MSH5p, MLH1p-MLH3p dependent pathway. We are interested in charactering the role of these complexes in crossover formation and distribution among all homolog pairs. Errors in this process are linked to congenital birth defects in humans such as Down's syndrome.Our laboratory is also interested in understanding the effect of genetic background on mutation rate variation using S. cerevisiae as a model. These studies are relevant for understanding cancer progression, genome evolution and architecture. We use high- throughput genomic methods as well as classical genetics to achieve these aims.

More at http://faculty.iisertvm.ac.in/~nishantkt/index.html

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

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

RopeBWT2: Incremental construction of FM-index for DNA sequences

Rahul Nayak — Thu, 25 Oct 2018 04:48:54 -0500

RopeBWT2 is an tool for constructing the FM-index for a collection of DNA sequences. It works by incrementally inserting one or multiple sequences into an existing pseudo-BWT position by position, starting from the end of the sequences. This algorithm can be largely considered a mixture of BCR and dynamic FM-index. Nonetheless, ropeBWT2 is unique in that it may implicitlysort the input into reverse lexicographical order (RLO) or reverse-complement lexicographical order (RCLO) while building the index.

Address of the bookmark: https://github.com/lh3/ropebwt2

LTR_Finder: an efficient program for finding full-length LTR retrotranspsons in genome sequences.

Neel — Sun, 13 Jan 2019 07:05:53 -0600

LTR_Finder is an efficient program for finding full-length LTR retrotranspsons in genome sequences.

The Program first constructs all exact match pairs by a suffix-array based algorithm and extends them to long highly similar pairs. Then Smith-Waterman algorithm is used to adjust the ends of LTR pair candidates to get alignment boundaries. These boundaries are subject to re-adjustment using supporting information of TG..CA box and TSRs and reliable LTRs are selected. Next, LTR_FINDER tries to identify PBS, PPT and RT inside LTR pairs by build-in aligning and counting modules. RT identification includes a dynamic programming to process frame shift. For other protein domains, LTR_FINDER calls ps_scan (from PROSITE, http://www.expasy.org/prosite/) to locate cores of important enzymes if they occur.

Address of the bookmark: https://github.com/xzhub/LTR_Finder

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

IgBLAST 1.17 is now available with improved identification of productive V gene sequences

Jit — Sun, 01 Nov 2020 16:52:58 -0600

A new release of IgBLAST (1.17), the popular package for classifying and analyzing immunoglobulin and T cell receptor sequences, is now available on the web and from the FTP site. The updated package is better at identifying productive V gene sequences. We added a new field , “V frame shift”, to the IgBLAST output to indicate whether the V gene translation frame contains a frame-shift. We have also updated the definition of a productive V(D)J sequence to now exclude those with internal frame shifts.

See the new IgBLAST manual on the NCBI GitHub site for more information on setting up and running IgBLAST.

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

Steps to find palindrome in genomes !

BioStar — Thu, 09 Mar 2023 02:56:54 -0600

Palindromes are sequences of nucleotides that read the same backward as forward. They can be present in genomes and have various biological functions. Here are some methods for discovering palindromes in genomes:

Direct sequence search: One of the simplest ways to discover palindromes is to search the genome sequence directly for palindromic sequences using pattern matching tools, such as regular expressions or string algorithms. This approach can be useful for discovering simple palindromes, but may miss more complex palindromic structures.
Dot plot analysis: Dot plot analysis is a graphical method that can be used to identify palindromic regions in a genome. It involves plotting the genome sequence against itself and examining the diagonal patterns that emerge. Palindromic regions will appear as symmetrical patterns along the diagonal.
Restriction enzyme analysis: Some restriction enzymes, such as EcoRI and HindIII, recognize palindromic sequences and cleave DNA at these sites. By digesting the genome with these enzymes and examining the resulting fragments, palindromic regions can be identified.
Next-generation sequencing: High-throughput sequencing technologies, such as PacBio and Oxford Nanopore, can generate long reads that can span entire palindromic regions. By mapping these reads to the genome, palindromic regions can be identified and characterized.
Comparative genomics: Comparing the genomes of related species can also reveal palindromic regions that are conserved across evolutionarily divergent lineages. This approach can help identify functional palindromes that are under selective pressure.

Overall, the discovery of palindromic sequences in genomes can be accomplished using a variety of methods, each with their own advantages and limitations. A combination of these methods can provide a comprehensive understanding of the palindromic landscape of a genome.