Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.

NCBI has developed an automatic prokaryotic genome annotation pipeline that combines ab initio gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP; see Pubmed Article) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume. You can find a more detailed description of the new version of the pipeline in NCBI Handbook chapter. NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.

https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

The arguments accepted by ScaffMatch are:

  -w) Working directory -- this is the directory where ScaffMatch files are stored. These are .sam files produced after mapping reads to contigs and the resulting scaffolds file `scaffolds.fa` fasta file;

  -c) Contig fasta file;

  -m) Command line argument with no options. It is used when .sam files are used instead of reads .fastq files. Do not use this option if you provide reads files;

  -1) (Comma separated list of) either .fastq or .sam file(s) corresponding to the first read of the read pair;

  -2) (Comma separated list of) either .fastq or .sam file(s) corresponding to the second read of the read pair;

  -i) (Comma separated list of) insert size(s) of the library(-ies);

  -s) (Comma separated list of) library(-ies) standard deviation(s) of insert size(s);

  -t) Bundle threshold. Pairs of contigs supported by number of read pairs less than the value of this argument are discarded. Optional argument, by default it is equal to 5;

  -g) Matching heuristics: use `max_weight` for Maximum Weight Matching heuristics with the Insertion step, use `backbone` for Maximum Weight Matching heuristics without the Insertion step, use `greedy` for Greedy Matching heuristics;

  -l) Log file - where to store the logs. Optional argument. By default, stdout is used.

Address of the bookmark: http://alan.cs.gsu.edu/NGS/?q=content/scaffmatch

Address of the bookmark: http://garm-meta-assem.sourceforge.net/

Address of the bookmark: https://github.com/mahulchak/quickmerge

Training Resources:

• Bioinformatics Workshop (Webinars)
• Bioinformatics Training Slides

Specialized Applications:

• De Novo Assembly
• Transcriptome analysis
• Base Modification Analysis
• Barcoding
• Data Analysis Tools
• Minor Variants and Phasing Analysis

Address of the bookmark: https://github.com/PacificBiosciences/Bioinformatics-Training/wiki

Address of the bookmark: http://abacas.sourceforge.net/Manual.html#9._Colour_code

Address of the bookmark: http://kakitone.github.io/finishingTool/

Warning! This software is to be considered under development. Functionality and the user interface may still change significantly from one version to another. If you want to use this software, please stay up to date with the list of known issues:https://github.com/BinPro/CONCOCT/issues

Address of the bookmark: https://github.com/BinPro/CONCOCT

Address of the bookmark: http://seqmule.openbioinformatics.org/en/latest/

Hagfish requires a reference sequence and a paired end re-sequencing data set. Hagfish has more power the larger the insert size of the paired end library is.

Quick links: Installation,Operation, Read mappers, Hagfish scripts, Hagfish plots

Address of the bookmark: https://github.com/mfiers/hagfish