BOL: Related items

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/

Cogent: a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences.

Jit — Tue, 18 Jun 2019 05:33:04 -0500

Cogent is a tool that identifies gene families and reconstructs the coding genome using high-quality transcriptome data without a reference genome, and can be used to check assemblies for the presence of these known coding sequences.

Cogent is a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences. It is designed to be used on Iso-Seq data and in cases where there is no reference genome or the ref genome is highly incomplete.

See a recent presentation on Cogent being applied to the Cuttlefish Iso-Seq data.

Cogent preliminary draft paper (updated 2016Dec version), Supplementary

Please see wiki for details on usage.

Address of the bookmark: https://github.com/Magdoll/Cogent

Sequence Tube Maps: displays multiple genomic sequences in the form of a tube map

Jit — Wed, 11 Mar 2020 01:12:06 -0500

A JavaScript module for the visualization of genomic sequence graphs. It automatically generates a "tube map"-like visualization of sequence graphs which have been created with vg. (https://github.com/vgteam/vg)

Link to working demo: https://vgteam.github.io/sequenceTubeMap/

Address of the bookmark: https://github.com/vgteam/sequenceTubeMap

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.

MACSE: Multiple Alignment of Coding SEquences Accounting for Frameshifts and Stop Codons

Jit — Mon, 18 Feb 2019 04:21:50 -0600

MACSE aligns coding NT sequences with respect to their AA translation while allowing NT sequences to contain multiple frameshifts and/or stop codons. MACSE is hence the first automatic solution to align protein-coding gene datasets containing non-functional sequences (pseudogenes) without disrupting the underlying codon structure. It has also proved useful in detecting undocumented frameshifts in public database sequences and in aligning next-generation sequencing reads/contigs against a reference coding sequence.

For further details about the underlying algorithm see the original publication:
MACSE: Multiple Alignment of Coding SEquences accounting for frameshifts and stop codons.
Vincent Ranwez, Sébastien Harispe, Frédéric Delsuc, Emmanuel JP Douzery
PLoS One 2011, 6(9): e22594.

Address of the bookmark: https://bioweb.supagro.inra.fr/macse/index.php?menu=releases

Protein function annotation and machine learning - UPMC - Paris, France

Sat, 02 Aug 2014 01:22:52 -0500

Protein function annotation and machine learning - UPMC - Paris, France

Job Description: We are interested in finding an excellent postdoc with interests in protein functional annotation, machine learning and computer grids. The position is open for 3.5 years at the Université Pierre et Marie Curie, in the heart of paris.

Research topic: Protein function annotation, multiple probabilistic models, domain architecture, machine learning, combinatorial optimization, computer grid.

Title: A novel integrative platform for large scale protein annotation that exploits a multitude of diversified probabilistic models in several protein signature databases.

We propose a novel integrated approach for large scale protein annotation that will exploit an unprecedented amount of genomic data as well as sophisticated machine learning techniques and combinatorial optimization approaches taking advantages of High Performance Computing (HPC) environments. The idea is to uncover as much as possible the evolutionary processes of protein sequences that took place throughout the whole tree of life and that affected the evolution of a protein family. We have already demonstrated in a previous work that the problem of functional annotation is inherent to the ability of uncovering such paths. Now, we shall extend this approach to large scale genome annotation by considering 11 different protein databases, constituted by about 10^9 protein sequences, and by producing a large pool of diversified probabilistic models coding for about 10^7 evolutionary protein pathways. Such models will be used to search for specific domains in genomes to be annotated. Our previous methodology needs to be fundamentally improved to deal with this large amount of biological data. In this project, we shall work on the algorithms to reduce the space of models and the search complexity, and we shall implement some important algorithmic changes towards the realization of a powerful integrated annotation tool.

Where: This project is run on the Laboratoire de Biologie Computationnelle et Quantitative UMR7238 CNRS-UPMC – Analytical Genomics team, headed by A.Carbone. It is co-advised with Pierre-Henri Wuillemin, Laboratoire d’Informatique de Paris 6 – Equipe DECISION.

Start date: September 1st, 2014
Contact Person: Alessandra Carbone
Contact: alessandra.carbone@lip6.fr

flo

Jitendra Narayan — Wed, 10 Feb 2016 10:52:32 -0600

flo - same species annotations lift over pipeline

Lift over is the process of transferring annotations from one genome assembly to another. Usually lift over is done because there is a new, improved genome assembly for the species and good quality annotations (maybe manually curated or experimentally verified) are available on the old assembly.

The idea is simple: align the new assembly with the old one (e.g., with BLAT), process the alignment data to define how a coordinate or coordinate range on the old assembly should be transformed to the new assembly (e.g., as a chain file), transform the coordinates (e.g., with liftOver).

https://github.com/wurmlab/flo

Address of the bookmark: https://github.com/wurmlab/flo

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/

Prokka: tool for the rapid annotation of prokaryotic genomes

Jit — Mon, 06 Mar 2017 03:49:57 -0600

Prokka is a software tool for the rapid annotation of prokaryotic genomes. A typical 4 Mbp genome can be fully annotated in less than 10 minutes on a quad-core computer, and scales well to 32 core SMP systems. It produces GFF3, GBK and SQN files that are ready for editing in Sequin and ultimately submitted to Genbank/DDJB/ENA.

Address of the bookmark: http://www.vicbioinformatics.com/software.prokka.shtml