BOL: Related items

SATSUMA : Highly sensitive whole-genome synteny alignments.

Jit — Fri, 13 May 2016 05:25:26 -0500

Satsuma is a whole-genome synteny alignment program. It takes two genomes, computes alignments, and then keeps only the parts that are orthologous, i.e. following the conserved order and orientation of features, such as protein coding genes, non-coding genes, or neutral sequences. Satsuma does not require any pre-processing, such as repeat masking, since it will automatically detect ambiguous mappings.

Satsuma has parallelization built-in and is designed to run on multi-core architectures. The run-time for aligning two bird-size genomes (~1.2 Gb) is around two days on 24 CPUs.

You can find the manual here.
Download the latest source code from here.
Stable versions can also be downloaded from the Broad Institute's web site.

An incomplete list of questions and answers (yes, these have really been asked by our users! Please feel free to add your own by e-mailing us) is here.

If you use Satsuma in your research, please cite:
Grabherr, M. G., Russell, P., Meyer, M., Mauceli, E., Alföldi, J., Di Palma, F., & Lindblad-Toh, K. (2010). Genome-wide synteny through highly sensitive sequence alignment: Satsuma. Bioinformatics, 26(9), 1145-51.

Tutorial at http://evomics.org/learning/genomics/satsuma/

Address of the bookmark: http://satsuma.sourceforge.net/

Kraken: ultrafast metagenomic sequence classification using exact alignments

Jit — Mon, 27 Jun 2016 11:01:44 -0500

Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/.

Krona

https://sourceforge.net/p/krona/home/krona/

Address of the bookmark: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/

FSA: Fast Statistical Alignment

Jit — Mon, 06 Feb 2017 04:26:01 -0600

FSA is a probabilistic multiple sequence alignment algorithm which uses a "distance-based" approach to aligning homologous protein, RNA or DNA sequences. Much as distance-based phylogenetic reconstruction methods like Neighbor-Joining build a phylogeny using only pairwise divergence estimates, FSA builds a multiple alignment using only pairwise estimations of homology. This is made possible by the sequence annealing technique for constructing a multiple alignment from pairwise comparisons, developed by Ariel Schwartz in "Posterior Decoding Methods for Optimization and Control of Multiple Alignments."

FSA brings the high accuracies previously available only for small-scale analyses of proteins or RNAs to large-scale problems such as aligning thousands of sequences or megabase-long sequences. FSA introduces several novel methods for constructing better alignments:

FSA uses machine-learning techniques to estimate gap and substitution parameters on the fly for each set of input sequences. This "query-specific learning" alignment method makes FSA very robust: it can produce superior alignments of sets of homologous sequences which are subject to very different evolutionary constraints.
FSA is capable of aligning hundreds or even thousands of sequences using a randomized inference algorithm to reduce the computational cost of multiple alignment. This randomized inference can be over ten times faster than a direct approach with little loss of accuracy.
FSA can quickly align very long sequences using the "anchor annealing" technique for resolving anchors and projecting them with transitive anchoring. It then stitches together the alignment between the anchors using the methods described above.
The included GUI, MAD (Multiple Alignment Display), can display the intermediate alignments produced by FSA, where each character is colored according to the probability that it is correctly aligned (see the picture and movie at the top of the page).

You can see more information on the FAQ.

Address of the bookmark: http://fsa.sourceforge.net/

Sequence - Evolution - Function; Computational Approaches in Comparative Genomics

Jit — Sun, 06 Aug 2017 06:58:12 -0500

Sequence - Evolution - Function is an introduction to the computational approaches that play a critical role in the emerging new branch of biology known as functional genomics. The book provides the reader with an understanding of the principles and approaches of functional genomics and of the potential and limitations of computational and experimental approaches to genome analysis.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/books/NBK20260/

ASAR: Advanced metagenomic Sequence Analysis in R

Jit — Mon, 09 Jul 2018 05:20:50 -0500

An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.

To read the manual, please click the link https://askarbek-orakov.github.io/ASAR/

Address of the bookmark: https://github.com/Askarbek-orakov/ASAR

Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome

BioStar — Wed, 22 Jul 2020 10:11:13 -0500

We have developed the Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome. We envisage its use during annotation jamborees, genome comparison and for use by developers for rapid feedback during annotation software development and testing. SOBA also provides annotation consistency feedback to ensure correct use of terminology within annotations, and guides users to add new terms to the Sequence Ontology when required. SOBA is available at http://www.sequenceontology.org/cgi-bin/soba.cgi.

More at https://pubmed.ncbi.nlm.nih.gov/20494974/

Address of the bookmark: http://www.sequenceontology.org/cgi-bin/soba.cgi

NCBIBLAST+ 2.14.1 now available

Neel — Wed, 30 Aug 2023 02:36:13 -0500

#NCBIBLAST+ 2.14.1 now available with improved documentation, faster and more reliable database downloads, and some bug fixes.

Check out the changes they made.

They added the cleanup-blastdb-volumes.py script to remove unused BLAST database volumes. Read the documentation here.

They also switched the protocol from ftp to https to access BLAST databases for increased performance and reliability when downloading data from the NCBI with the update_blastdb.pl script.

And fixed a few bugs related to downloading data from the NCBI, and mt_mode crashing blastn and blastx.

Check out the release notes.

Download BLAST+ 2.14.1

Questions or comments? Please write the BLAST help desk.

More info and download: https://blast.ncbi.nlm.nih.gov/doc/blast-news/2023-BLAST-News.html

Scripts for the analysis of HGT in genome sequence data.

Jit — Wed, 29 Nov 2017 16:44:10 -0600

Scripts for the analysis of HGT in genome sequence data

Address of the bookmark: https://github.com/reubwn/hgt

Many-to-many pairwise alignments of two sequence sets

Poonam Mahapatra — Tue, 19 Jun 2018 08:34:15 -0500

needleall reads a set of input sequences and compares them all to one or more sequences, writing their optimal global sequence alignments to file. It uses the Needleman-Wunsch alignment algorithm to find the optimum alignment (including gaps) of two sequences along their entire length. The algorithm uses a dynamic programming method to ensure the alignment is optimum, by exploring all possible alignments and choosing the best. A scoring matrix is read that contains values for every possible residue or nucleotide match. Needleall finds the alignment with the maximum possible score where the score of an alignment is equal to the sum of the matches taken from the scoring matrix, minus penalties arising from opening and extending gaps in the aligned sequences. The substitution matrix and gap opening and extension penalties are user-specified.

Address of the bookmark: http://emboss.sourceforge.net/apps/release/6.6/emboss/apps/needleall.html

iRNAD: a computational tool for identifying D modification sites in RNA sequence

Abhimanyu Singh — Thu, 16 May 2019 00:20:07 -0500

iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification.

http://lin-group.cn/server/iRNAD/

Address of the bookmark: http://lin-group.cn/server/iRNAD/