BOL: Related items

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

Flanker

Rahul Nayak — Sat, 27 Feb 2021 22:04:53 -0600

Flanker, a Python package which performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of mobile genetic elements (MGEs) without prior knowledge of their structure. Flanker can be flexibly parameterised to finetune outputs by characterising upstream and downstream regions separately and investigating variable lengths of flanking sequence.

Address of the bookmark: https://github.com/wtmatlock/flanker

BioGPS

Anjana — Mon, 23 May 2016 03:15:46 -0500

A free extensible and customizable gene annotation portal, a complete resource for learning about gene and protein function.

http://biogps.org/#goto=welcome

Address of the bookmark: http://biogps.org/#goto=welcome

Tools for Protein-Protein Docking !

Poonam Mahapatra — Wed, 25 Apr 2018 05:15:53 -0500

Predicting the structure of protein–protein complexes using docking approaches is a difficult problem whose major challenges include identifying correct solutions, and properly dealing with molecular flexibility and conformational changes. Following are the tools to predict the structure of protein–protein complexes:

3D-Dock Suite

Global rigid search: FFTShape complementarity and electrostatics

Re-scoring and clustering. Refinement of interface side-chains

3D-Garden

Global rigid search in ensamble

Shape complementarity and Lennard–Jones potential

Side chain and backbone dihedral refinement

DOT

Global rigid search: FFTShape complementarity, electrostatics and VDWNone

Escher NG

Global rigid searchShape complementarity, hydrogen bonds and electrostatic

Integrated in VEGA

GRAMM

Global rigid search: FFT. smooth protein surface representation for soft docking

Shape complementarity and Lennard-Jones potential

Clustering of conformations

GRAMM-X

Global rigid search: FFT. smooth protein surface representation for soft docking

Shape complementarity and Lennard-Jones potentialminimization and re-scoring with multiple filters

HEX

Global rigid search: Fourier correlation of spherical harmonics

Shape complementarity

HADDOCK

Global rigid searchElectrostatic ,VDW and desolvation energy termsMD simulated annealing refinement . Filtering based on external data.

ICM

Global rigid search: Monte CarloEmpirical scoring function

Clustering and selection of conformations. Refinement of interface side-chains and re-scoring

MolFit

Global rigid search: FFTShape complementarity

Clustering of good solutions, filtering using a priori information and small, local rigid rotations around selected conformations

PatchDock

Global rigid searchShape complementarity and atomic desolvation energy

Clustering of conformations

PyDock

Global rigid search:FFTShape complementarity

rescoring by binding electrostatics and desolvation energy

RosettaDock

Local rigid search: Monte Carlo with low and high resolution structure representation levels

Different scoring parameters for the different resolutions

ZDOCK

Global rigid search: FFTShape complementarity, desolvation energy, and electrostatics.

Energy minimization and re-scoringFree for academics

Point to note:

The proper treatment of flexibility in protein–protein docking is still an active field of research. You first should analyzed your proteins in order to define their conformational space and then choose the most suitable method for your docking problem.

Genome Annotation Transfer Utility (GATU)

Jit — Mon, 29 May 2017 05:54:53 -0500

Genome Annotation Transfer Utility (GATU) was designed to facilitate quick, efficient annotation of similar genomes using genomes that have already been annotated. For example, whenever a new strain of SARS coronavirus is sequenced, it is possible, using GATU, to automatically annotate the new strain using a previously-annotated strain of SARS CoV. This saves researchers from tedious manual annotation of these sequences.

The program utilizes tBLASTn and BLASTn algorithms to map genes from the reference genome (the annotated strain) to the new sequence (the unannotated strain). The goal is to annotate the majority of the new genome’s genes in a single step. ORFs present in the target genome and absent from the reference genome are also identified; these ORFs can be further analyzed using BLAST, VGO and BBB. Afterwards, they can either be accepted for/rejected from annotation. GATU can handle multiple-exon genes as well as mature peptides. Although it was designed for use with viral genomes, GATU can also be used to help annotate larger genomes (ie. bacterial genomes).

The output is saved in GenBank, XML, or EMBL file format.

Address of the bookmark: https://virology.uvic.ca/help/tool-help/help-books/genome-annotation-transfer-utility-gatu-documentation/