More at http://gkno.me/

Address of the bookmark: http://gkno.me/

• For each genome, obtain a set of exon annotations. These annotations can be a combination of both exon predictions (e.g. Genscan) and annotations that have been experimentally verified (e.g. RefSeq). Ideally, we would like to have these annotations be as sensitive as possible. Specificity is not a concern, as incorrect annotations are not likely not have significant alignments with other gene annotations.
• Compare all exons against all exons in other genomes and record significant alignments between exons. Currently, we use BLAT to do this all-vs-all comparison with alignments being performed in protein space.
• Construct a graph with each vertex corresponding to a exon and edges between vertices whose corresponding exons have significant alignments.
• Identify cliques in this graph. These cliques are potential anchors to be used in the map.
• Starting with the largest cliques (those that have exons in all or most of the genomes), join neighboring (adjacent in genomic coordinates, in each genome) cliques to form runs. Smaller cliques that are inconsistent with runs formed by larger cliques are filtered out. After the smallest cliques have been considered, cliques that are not part of a run are discarded.
• The extents of each run in each genome are outputted as orthologous segments. The cliques from each run are used to output the exact genomic coordinates of anchors within each orthologous segment. These anchors can be used by genomic alignment programs (such as MAVID) to do a detailed alignment of each orthologous segment.

https://www.biostat.wisc.edu/~cdewey/mercator/

Address of the bookmark: https://www.biostat.wisc.edu/~cdewey/mercator/

If you have any questions or comments, post them on one of the trackers on BRIG’s SourceForge page:http://sourceforge.net/tracker/?group_id=328245.

Features:

• Images show similarity between a central reference sequence and other sequences as concentric rings.
• BRIG will perform all BLAST comparisons and file parsing automatically via a simple GUI.
• Contig boundaries and read coverage can be displayed for draft genomes; customized graphs and annotations can be displayed.
• Using a user-defined set of genes as input, BRIG can display gene presence, absence, truncation or sequence variation in a set of complete genomes, draft genomes or even raw, unassembled sequence data.
• BRIG also accepts SAM-formatted read-mapping files enabling genomic regions present in unassembled sequence data from multiple samples to be compared simultaneously

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

Address of the bookmark: https://sourceforge.net/projects/pb-jelly/

Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes

Mads Albertsen, Philip Hugenholtz, Adam Skarshewski, Gene W. Tyson, Kåre L. Nielsen and Per .H. Nielsen

Nature Biotechnology 2013, doi: 10.1038/nbt.2579

Address of the bookmark: https://github.com/MadsAlbertsen/multi-metagenome

Address of the bookmark: http://ritchielab.psu.edu/software/phenogram-downloads

Our work is published in Scientific Reports:

Ye, C. et al. DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies. Sci. Rep. 6, 31900; doi: 10.1038/srep31900 (2016).

http://www.nature.com/articles/srep31900

The manual can be downloaded from:

https://github.com/yechengxi/DBG2OLC/raw/master/Manual.docx

To use precompiled versions,please go to:

https://github.com/yechengxi/DBG2OLC/tree/master/compiled

Address of the bookmark: https://github.com/yechengxi/DBG2OLC

https://github.com/jennomics/WorkflowPaper/blob/master/Genome%20Assembly%20and%20Annotation.md

Address of the bookmark: http://bioinformatics.uconn.edu/bacterial-genome-assembly-tutorial/

• hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in output for easy plotting.
• gcp: K-mer GC Processor. Creates a matrix of the number of K-mers found given a GC count and a K-mer count.
• comp: K-mer comparison tool. Creates a matrix of shared K-mers between two (or three) sequence files or hashes.
• sect: SEquence Coverage estimator Tool. Estimates the coverage of each sequence in a file using K-mers from another sequence file.
• blob: Given, reads and an assembly, calculates both the read and assembly K-mer coverage along with GC% for each sequence in the assembly.SEquence Coverage estimator Tool.
• filter: Filtering tools. Contains tools for filtering k-mer hashes and FastQ/A files:
  • kmer: Produces a k-mer hash containing only k-mers within specified coverage and GC tolerances.
  • seq: Filters a sequence file based on whether or not the sequences contain k-mers within a provided hash.
• plot: Plotting tools. Contains several plotting tools to visualise K-mer and compare distributions. The following plot tools are available:
  • density: Creates a density plot from a matrix created with the "comp" tool. Typically this is used to compare two K-mer hashes produced by different NGS reads.
  • profile: Creates a K-mer coverage plot for a single sequence. Takes in fasta coverage output coverage from the "sect" tool
  • spectra-cn: Creates a stacked histogram using a matrix created with the "comp" tool. Typically this is used to compare a jellyfish hash produced from a read set to a jellyfish hash produced from an assembly. The plot shows the amount of distinct K-mers absent, as well as the copy number variation present within the assembly.
  • spectra-hist: Creates a K-mer spectra plot for a set of K-mer histograms produced either by jellyfish-histo or kat-histo.
  • spectra-mx: Creates a K-mer spectra plot for a set of K-mer histograms that are derived from selected rows or columns in a matrix produced by the "comp".

In addition, KAT contains a python script for analysing the mathematical distributions present in the K-mer spectra in order to determine how much content is present in each peak.

This README only contains some brief details of how to install and use KAT. For more extensive documentation please visit: https://kat.readthedocs.org/en/latest/

https://academic.oup.com/bioinformatics/article/33/4/574/2664339 

Address of the bookmark: https://github.com/TGAC/KAT

Perl one-liners are small and awesome Perl programs that fit in a single line of code and they do one thing really well. These things include changing line spacing, numbering lines, doing calculations, converting and substituting text, deleting and printing certain lines, parsing logs, editing files in-place, doing statistics, carrying out system administration tasks, updating a bunch of files at once, and many more. Perl one-liners will make you the shell warrior. Anything that took you minutes to solve, will now take you seconds!

perl -pe '$\="\n"'   
#double space a file

perl -pe '$_ .= "\n" unless /^$/'
#double space a file except blank lines

perl -pe '$_.="\n"x7'
#7 space in a line.

perl -ne 'print unless /^$/'
#remove all blank lines

perl -lne 'print if length($_) < 20'
#print all lines with length less than 20.

perl -00 -pe ''
#If there are multiple spaces, delete all leaving one(make the file a single spaced file).

perl -00 -pe '$_.="\n"x4'
#Expand single blank lines into 4 consecutive blank lines

perl -pe '$_ = "$. $_"'
#Number all lines in a file

perl -pe '$_ = ++$a." $_" if /./'
#Number only non-empty lines in a file

perl -ne 'print ++$a." $_" if /./'
#Number and print only non-empty lines in a file

perl -pe '$_ = ++$a." $_" if /regex/'
#Number only lines that match a pattern

perl -ne 'print ++$a." $_" if /regex/'
#Number and print only lines that match a pattern

perl -ne 'printf "%-5d %s", $., $_ if /regex/'
#Left align lines with 5 white spaces if matches a pattern (perl -ne 'printf "%-5d %s", $., $_' : for all the lines)

perl -le 'print scalar(grep{/./}<>)'
#prints the total number of non-empty lines in a file

perl -lne '$a++ if /regex/; END {print $a+0}'
#print the total number of lines that matches the pattern

perl -alne 'print scalar @F'
#print the total number fields(words) in each line.

perl -alne '$t += @F; END { print $t}'
#Find total number of words in the file

perl -alne 'map { /regex/ && $t++ } @F; END { print $t }'
#find total number of fields that match the pattern

perl -lne '/regex/ && $t++; END { print $t }'
#Find total number of lines that match a pattern

perl -le '$n = 20; $m = 35; ($m,$n) = ($n,$m%$n) while $n; print $m'
#will calculate the GCD of two numbers.

perl -le '$a = $n = 20; $b = $m = 35; ($m,$n) = ($n,$m%$n) while $n; print $a*$b/$m'
#will calculate lcd of 20 and 35.

perl -le '$n=10; $min=5; $max=15; $, = " "; print map { int(rand($max-$min))+$min } 1..$n'
#Generates 10 random numbers between 5 and 15.

perl -le 'print map { ("a".."z",”0”..”9”)[rand 36] } 1..8'
#Generates a 8 character password from a to z and number 0 – 9.

perl -le 'print map { ("a",”t”,”g”,”c”)[rand 4] } 1..20'
#Generates a 20 nucleotide long random residue.

perl -le 'print "a"x50'
#generate a string of ‘x’ 50 character long

perl -le 'print join ", ", map { ord } split //, "hello world"'
#Will print the ascii value of the string hello world.

perl -le '@ascii = (99, 111, 100, 105, 110, 103); print pack("C*", @ascii)'
#converts ascii values into character strings.

perl -le '@odd = grep {$_ % 2 == 1} 1..100; print "@odd"'
#Generates an array of odd numbers.

perl -le '@even = grep {$_ % 2 == 0} 1..100; print "@even"'
#Generate an array of even numbers

perl -lpe 'y/A-Za-z/N-ZA-Mn-za-m/' file
#Convert the entire file into 13 characters offset(ROT13)

perl -nle 'print uc'
#Convert all text to uppercase:

perl -nle 'print lc'
#Convert text to lowercase:

perl -nle 'print ucfirst lc'
#Convert only first letter of first word to uppercas

perl -ple 'y/A-Za-z/a-zA-Z/'
#Convert upper case to lower case and vice versa

perl -ple 's/(\w+)/\u$1/g'
#Camel Casing

perl -pe 's|\n|\r\n|'
#Convert unix new lines into DOS new lines:

perl -pe 's|\r\n|\n|'
#Convert DOS newlines into unix new line

perl -pe 's|\n|\r|'
#Convert unix newlines into MAC newlines:

perl -pe '/regexp/ && s/foo/bar/'
#Substitute a foo with a bar in a line with a regexp.

Reference/Sources:

http://genomics-array.blogspot.in/2010/11/some-unixperl-oneliners-for.html

http://genomespot.blogspot.com/2013/08/a-selection-of-useful-bash-one-liners.html

http://biowize.wordpress.com/2012/06/15/command-line-magic-for-your-gene-annotations/

http://genomics-array.blogspot.com/2010/11/some-unixperl-oneliners-for.html

http://bioexpressblog.wordpress.com/2013/04/05/split-multi-fasta-sequence-file/