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 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?

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

Main features of YASS are:

• multiple, possibly overlapping seeds and a new hit criterion to ensure a good sensitivity/selectivity trade-off
• transition-constrained spaced seeds to improve sensitivity (transition mutations are purine to purine [A<->G] or pyrimidine to pyrimidine [C<->T])
• using different scoring schemes with bit-score and E-value evaluated according to the sequence background frequencies
• parameterizable output filter for low complexity repeats
• reporting of various alignment statistical parameters (mutation bias along triplets, transition/transversion)
• post-processing step to group gapped alignments

Address of the bookmark: http://bioinfo.lifl.fr/yass/

Progressive Cactus is a whole-genome alignment package.

Requirements

• git
• gcc 4.2 or newer
• python 2.7
• wget
• 64bit processor and build environment
• 150GB+ of memory on at least one machine when aligning mammal-sized genomes; less memory is needed for smaller genomes.
• Parasol or SGE for cluster support.
• 750M disk space

Instructions

IMPORTANT NOTE: Progressive Cactus does not presently support installation into paths that contain spaces. Until this is resolved, you can use a softlink as a workaround: ln -s "path with spaces" "installation path without spaces"

In the parent directory of where you want Progressive Cactus installed:

git clone git://github.com/glennhickey/progressiveCactus.git
cd progressiveCactus
git pull
git submodule update --init
make

It is also convenient to add the location of progressiveCactus/bin to your PATH environment variable. In order to run the included tools (ex hal2maf) in the submodules/ directory structure, first source progressiveCactus/environment to load the installed environment.

If any errors occur during the build process, you are unlikely to be able to use the tool. Please submit a GitHub issue so we can help out: not only will you help yourself, but others who wish to use the tool as well.

Note that all dependencies are also built and included in the submodules/ directory. This increases the size and build time but greatly simplifies installation and version management. The installation does not create or modify any files outside the progressiveCactus/ directory.

Address of the bookmark: https://github.com/glennhickey/progressiveCactus

Address of the bookmark: http://4dgenome.research.chop.edu/

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

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

• Handle big sequence data, e.g:
  • Compare two vertebrate genomes
  • Align billions of DNA reads to a genome
• Indicate the reliability of each aligned column.
• Use sequence quality data properly.
• Compare DNA to proteins, with frameshifts.
• Compare PSSMs to sequences
• Calculate the likelihood of chance similarities between random sequences.
• Do split and spliced alignment.
• Train alignment parameters for unusual kinds of sequence (e.g. nanopore).

Address of the bookmark: http://last.cbrc.jp/

It demonstrates how to use long PacBio sequencing reads to assemble a bacterial genome, and includes additional steps for circularising, trimming, finding plasmids, and correcting the assembly with short-read Illumina data.

 Please comment if you know any other long read addembly tutorial.

Address of the bookmark: http://sepsis-omics.github.io/tutorials/modules/cmdline_assembly_v2/

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ