BOL: Related items

Bioinformatics tools and software

Jit — Tue, 05 Jul 2016 10:02:26 -0500

USEARCH >
Extreme high-throughput sequence analysis. Orders of magnitude faster than BLAST. MUSCLE >
Multiple sequence alignment. Faster and more accurate than CLUSTALW.

UPARSE >
OTU clustering for 16S and other marker genes. Highly accurate OTU sequences and improved diversity measures. UCHIME >
Chimeric sequence detection. PILER >
De novo genome repeat finder. PILER-CR >
Detection of CRISPR repeats in bacterial genomes. QSCORE >
Compare two multiple alignments for benchmarking. PALS >
Whole-genome alignment. PREFAB >
Protein Reference Alignment Database. MSA benchmark collection >
Selected multiple alignment benchmarks in a standardized FASTA format.

Address of the bookmark: http://drive5.com/software.html

MEGAN6

Neel — Mon, 25 Jul 2016 05:45:22 -0500

Microbiome analysis using a single application

MEGAN6 is a comprehensive toolbox for interactively analyzing microbiome data. All the interactive tools you need in one application.

Taxonomic analysis using the NCBI taxonomy or a customized taxonomy such as SILVA
Functional analysis using InterPro2GO, SEED, eggNOG or KEGG
Bar charts, word clouds, Voronoi tree maps and many other charts
PCoA, clustering and networks
Supports metadata
MEGAN parses many different types of input

Why use MEGAN6?

The software is:

Easy to use. MEGAN6 is a single application and all features are available through menus, toolbars and graphics. No scripting skills required.
Powerful. MEGAN6 allows you to work with hundreds of samples containing hundreds of millions of sequencing reads. Blast-like analysis can be performed using DIAMOND.
Comprehensive. MEGAN6 offers a large range of analysis tools, and is under active development.

Address of the bookmark: https://ab.inf.uni-tuebingen.de/software/megan6

OrganellarGenomeDRAW

Jit — Tue, 16 Aug 2016 08:13:13 -0500

OrganellarGenomeDRAW is dedicated to convert genetic information stored in GenBank entries to graphical maps. The input text file has to be in GenBank flat file format, whereas the output format can be chosen among several formats. The application is especially optimized and adapted for the creation of high-quality, detailed circular maps of organellar genomes like the plastid genome (plastome) or the mitochondrial genome (chondriome). Nevertheless, you can upload any GenBank entry. The workflow is devided into three steps.

More at http://ogdraw.mpimp-golm.mpg.de/cgi-bin/ogdraw.pl

Address of the bookmark: http://ogdraw.mpimp-golm.mpg.de/index.shtml

GeneMANIA

Jit — Mon, 22 Aug 2016 09:55:16 -0500

Faster, more accurate algorithms function prediction "GeneMANIA (Multiple Association Network Integration Algorithm)" have however been developed in recent years and are publicly available on the web, indicating the future direction of function prediction.

Address of the bookmark: http://genemania.org/

Gene Finding and Predictions

Poonam Mahapatra — Fri, 26 Aug 2016 07:26:27 -0500

In this exercise, a previously annotated gene will be used to measure the accuracy of different gene finding approaches. GRAIL, GENSCAN, geneid, FGENESH, GenomeScan, GrailEXP and GENEWISE will be used to annotate the sequence. Both search by signal, content and homology (protein and cDNA sequences) methods will be employed in order to improve the ab initio results. Weak conservation of Start codons will lead to wrong prediction of initial exons in most cases.

http://genome.crg.es/courses/Bioinformatics2003_genefinding/

Address of the bookmark: http://genome.crg.es/courses/Bioinformatics2003_genefinding/

Artemis Comparison Tool (ACT)

Shruti Paniwala — Wed, 07 Sep 2016 03:54:41 -0500

ACT is a Java application for displaying pairwise comparisons between two or more DNA sequences. It can be used to identify and analyse regions of similarity and difference between genomes and to explore conservation of synteny, in the context of the entire sequences and their annotation. It can read complete EMBL, GENBANK and GFF entries or sequences in FASTA or raw format.

Address of the bookmark: http://www.sanger.ac.uk/science/tools/artemis-comparison-tool-act

CIRCOS Visualize !!

Jit — Fri, 02 Sep 2016 08:29:26 -0500

Before uploading a data file, check the samples gallery to make sure that your data format is compatible.

Your file must be plain text.
Your data values must be non-negative integers.
Data must be space-separated (one or more tab or space, which will be collapsed).
No two rows or columns may have the same name.
Column and row names must begin with a letter (e.g. 'A', 'A0', 'A-0') and can only contain letters, numbers and _. No punctuation!
Maximum row + column total is 150 — if exceeded, rows and columns are limited to 75.
If you are using order, size and color rows/columns in combination they must appear in that order.

Need help? Post questions to the Circos Google Group.

http://mkweb.bcgsc.ca/tableviewer/visualize/

Address of the bookmark: http://mkweb.bcgsc.ca/tableviewer/visualize/

Sybil

Jit — Wed, 07 Sep 2016 03:20:44 -0500

The Sybil software package provides a primarily web-based front-end to comparative genome datasets warehoused in a chado relational database. It was developed by the bioinformatics department at The Institute for Genomic Research (TIGR) and development continues at the J. Craig Venter Institute (JCVI) and the Institute for Genome Sciences (IGS) at the University of Maryland: Baltimore. Sybil has been used at TIGR/JCVI, IGS, NYU, New York Medical College, Novartis Vaccines and University of Maryland: College Park to support a number of research projects that involve comparative genome analysis. The following sections provide some high-level technical details about the overall architecture and external dependencies of the Sybil package.

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

Nemo – A stochastic, individual-base, genetically explicit simulation platform

Jit — Sat, 01 Oct 2016 14:45:02 -0500

A recombination map has been added for all multi-locus traits. The map positions (chromosomal) for neutral markers (e.g. SNPs) and loci under selection (QTLs, deleterious mutations, DMIs) can now be specified explicitly, or set at random. The map can hold an unlimited number of loci of different types jointly, at any recombination scale (cM or lower). The effects of linkage can thus be finely explored.
A new trait coding for (Bateson-)Dobzhansky-Muller incompatibility loci. Multiple haploid or diploid pairs of incompatible loci can be spread throughout the genome and affect individual fitness.
Multi-type selection: Individual fitness can be jointly determined by different types of loci under selectinon, such as QTLs coding for quantitative traits under spatially variable selection, universally deleterious mutations, and Dobzhansky-Muller incompatibility loci.
An unlimited number of quantitative traits under different forms of selection can be modelled, based on universally pleiotropic loci with several bi- or multi-allelic models.
Spatial and temporal variation of selection on quantitative traits is possible, modelling shifts of environmental conditions over time.
The dispersal matrix describing the movement of individuals among sub-populations can be replaced by a connectivity matrix and a reduced dispersal matrix describing migration only among the connected sub-populations. This offers a substantial gain in computing time and system memory when simulating very large grids.
Input parameters' arguments may be specified in separate files. This is particularly convenient when specifying large matrices.
Many adjustments have been made for refined control of the input of parameters and data output. See updates in the manual.

Address of the bookmark: http://nemo2.sourceforge.net/index.html

PHYMMBL

Jit — Mon, 10 Oct 2016 08:56:34 -0500

Metagenomics sequencing projects collect samples of DNA from uncharacterized environments that may contain hundreds or even thousands of species. One of the main challenges in analyzing a metagenome is phylogenetic classification of raw sequence reads into groups representing the same or similar species. Such classification is a useful prerequisite for genome assembly and for analysis of the biological diversity present in a sample. The newest sequencing technologies have simultaneously made metagenomics easier, by making the sequencing process faster, and more difficult, by producing shorter read lengths than previous technologies. Methods for classifying sequences as short as 100 base pairs (bp) have until now been relatively inaccurate, requiring metagenomics projects to use older, long-read technologies. Phymm, a new classification approach for metagenomics data which uses interpolated Markov models (IMMs) to taxonomically classify DNA sequences, can accurately classify reads as short as 100 bp. Its accuracy for short reads represents a significant leap forward over previous composition-based classification methods. PhymmBL (rhymes with "thimble"), the hybrid classifier included in this distribution which combines analysis from both Phymm and BLAST, produces even higher accuracy.

Address of the bookmark: http://www.cbcb.umd.edu/software/phymm/