Note that since the original publication, we have made significant changes to OPERA (v1.0 onwards) including refinements to its basic algorithm (to reduce local errors, improve efficiency etc.) and incorporated features that are important for scaffolding large genomes (multi-library support, better repeat-handling etc.), in addition to other scalability and usability improvements (bam and gzip support, smaller memory footprint). We therefore encourage you to download and use our latest version: OPERA-LG. In our benchmarks, it has significantly improved corrected N50 and reduced the number of scaffolding errors. Furthermore, our latest release contains the wrapper script OPERA-long-read that enables scaffolding with long-reads from third-generation sequencing technologies (PacBio or Oxford Nanopore). The manuscript describing the new features and algorithms is available at Genome Biology. We look forward to getting your feedback to improve it further.

Address of the bookmark: https://sourceforge.net/p/operasf/wiki/The%20OPERA%20wiki/

Address of the bookmark: http://www.bioconductor.org/packages/release/bioc/html/GeneBreak.html

More at http://biorxiv.org/content/early/2016/09/21/076463 

Address of the bookmark: http://shinyheatmap.com/

This is necessary in light of the increasing use of higher level statistics in biomedical research. I hope it accomplishes this mission and encourage its free distribution and use as a course text or supplement.

K Seefeld, May 2007

More at https://sourceforge.net/projects/record-genome-assembler/files/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pubmed/26558255

Download

Git repository of SWALO is at https://github.com/atifrahman/SWALO.

Address of the bookmark: https://atifrahman.github.io/SWALO/

Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.

If you use cutadapt, please cite DOI:10.14806/ej.17.1.200 .

More at https://github.com/marcelm/cutadapt

Address of the bookmark: http://cutadapt.readthedocs.io/en/stable/guide.html

http://compbio.cs.toronto.edu/prism/

Address of the bookmark: http://compbio.cs.toronto.edu/prism/

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

• paired-end (PE) and/or mate-pair libraries (NGS-based mode)
• long reads (NGS-based mode)
• synteny to the genome of some related species (reference-based mode)

Scaffolding 

In reference-based mode, pyScaf uses synteny to the genome of closely related species in order to order contigs and estimate distances between adjacent contigs.

Contigs are aligned globally (end-to-end) onto reference chromosomes, ignoring:

• matches not satisfying cut-offs (--identity and --overlap)
• suboptimal matches (only best match of each query to reference is kept)
• and removing overlapping matches on reference.

In preliminary tests, pyScaf performed superbly on simulated heterozygous genomes based on C. parapsilosis (13 Mb; CANPA) and A. thaliana (119 Mb; ARATH) chromosomes, reconstructing correctly all chromosomes always for CANPA and nearly always for ARATH (Figures in dropbox, CANPA table, ARATH table).
Runs took ~0.5 min for CANPA on 4 CPUs and ~2 min for ARATH on 16 CPUs.

Important remarks:

• Reduce your assembly before (fasta2homozygous.py) as any redundancy will likely break the synteny.
• pyScaf works better with contigs than scaffolds, as scaffolds are often affected by mis-assemblies (no de novo assembler / scaffolder is perfect...), which breaks synteny.
• pyScaf works very well if divergence between reference genome and assembled contigs is below 20% at nucleotide level.
• pyScaf deals with large rearrangements ie. deletions, insertion, inversions, translocations. Note however, this is experimental implementation!
• Consider closing gaps after scaffolding.

Address of the bookmark: https://github.com/lpryszcz/pyScaf