<![CDATA[BOL: Neel's pages]]> https://bioinformaticsonline.com/pages/owner/neelam? https://bioinformaticsonline.com/pages/view/44371/steps-to-find-all-the-repeats-in-the-genome Thu, 31 Aug 2023 02:43:28 -0500 https://bioinformaticsonline.com/pages/view/44371/steps-to-find-all-the-repeats-in-the-genome <![CDATA[Steps to find all the repeats in the genome !]]>

To find repeats in a genome from 2 to 9 length using a Perl script, you can use the RepeatMasker tool with the "--length" option[0]. Here's a step-by-step guide:

  1. Install RepeatMasker: First, you need to install RepeatMasker on your system. You can download it from the RepeatMasker website[0].
  1. Prepare the genome sequence: Make sure you have the genome sequence in a FASTA file format. Let's assume the file is named "genome.fasta".

./RepeatMasker -pa <number_of_processors> -nolow -norna -no_is -div <divergence_value> -lib RepeatMaskerLib.embl -gff -xsmall -small -poly -species <species_name> -dir <output_directory> -length <min_length>-<max_length> genome.fasta

Replace the following placeholders with appropriate values:

  • <number_of_processors>: The number of processors/threads you want to use for parallel processing.
  • <divergence_value>: The divergence value for the species you are analyzing. You can find divergence values for different species in the RepeatMasker documentation[0].
  • <species_name>: The name of the species you are analyzing.
  • <output_directory>: The directory where you want the output files to be saved.
  • <min_length> and <max_length>: The minimum and maximum lengths of the repeats you want to find (in this case, 2 and 9).
  1. Analyze the output: RepeatMasker will generate several output files, including a .out file. You can parse this file to extract the information you need. There is a Perl tool called "one_code_to_find_them_all.pl" that can help you parse RepeatMasker output files[0]. You can download it from the source provided.
  1. Use the provided Perl script: Once you have the "one_code_to_find_them_all.pl" script, you can run it to conveniently parse the RepeatMasker output files. Here's an example of how to use it:

perl one_code_to_find_them_all.pl --rm <RepeatMasker_out_file> --length <length_file>

 

Replace <RepeatMasker_out_file> with the path to your RepeatMasker .out file, and <length_file> with the path to a file containing the lengths of the reference elements.

This script will generate several output files, including .log.txt and .copynumber.csv, which contain quantitative information about the identified repeat elements.

Remember to adjust the parameters and options according to your specific needs and the characteristics of your genome.

]]> Neel https://bioinformaticsonline.com/pages/view/35915/iupac-codes Tue, 13 Mar 2018 05:16:05 -0500 https://bioinformaticsonline.com/pages/view/35915/iupac-codes <![CDATA[IUPAC codes]]> IUPAC codes

DNA:

Nucleotide Code: Base:

---------------- -----

A.................Adenine

C.................Cytosine

G.................Guanine

T (or U)..........Thymine (or Uracil)

R.................A or G

Y.................C or T

S.................G or C

W.................A or T

K.................G or T

M.................A or C

B.................C or G or T

D.................A or G or T

H.................A or C or T

V.................A or C or G

N.................any base . or -............gap

Protein:

Amino Acid Code: Three letter Code: Amino Acid:

---------------- ------------------ -----------

A.................Ala.................Alanine

B.................Asx.................Aspartic acid or Asparagine

C.................Cys.................Cysteine

D.................Asp.................Aspartic Acid

E.................Glu.................Glutamic Acid

F.................Phe.................Phenylalanine

G.................Gly.................Glycine

H.................His.................Histidine

I.................Ile.................Isoleucine

K.................Lys.................Lysine

L.................Leu.................Leucine

M.................Met.................Methionine

N.................Asn.................Asparagine

P.................Pro.................Proline

Q.................Gln.................Glutamine

R.................Arg.................Arginine

S.................Ser.................Serine

T.................Thr.................Threonine

V.................Val.................Valine

W.................Trp.................Tryptophan

X.................Xaa.................Any amino acid

Y.................Tyr.................Tyrosine

Z.................Glx.................Glutamine or Glutamic acid

]]> Neel https://bioinformaticsonline.com/pages/view/35395/comprehensive-list-of-visualization-tools-for-biological-pathways Tue, 30 Jan 2018 06:01:31 -0600 https://bioinformaticsonline.com/pages/view/35395/comprehensive-list-of-visualization-tools-for-biological-pathways <![CDATA[Comprehensive list of visualization tools for biological pathways]]> The study of biological pathways is a key to understand the different processes inside a cell: proteins exert their function not in isolation but in a tightly controlled network of interactions and reactions. Activation of a pathway typically leads to a change of state in the cell. Pathways come in different flavors, depending on their functions in the cell – the three main types are metabolic pathways, gene regulatory pathways, and signaling pathways. These biological pathways and networks are not only an appropriate approach to visualize molecular reactions. They have also become one leading method in -omics data analysis and visualization.

image

Following are the comprehensive list of visualization tools for biological pathways:

BiNA

Drawings of metabolic networks supporting hiding of cofactors and drawing of chemical structures

http://bina.unipax.info/

BioTapestry

Interactive tool for building, visualizing and sharing gene regulatory network models over the web

http://www.biotapestry.org/

Caleydo

Visual analysis framework targeted at biomolecular data. Visualization of interdependencies between multiple datasets

http://www.caleydo.org/

CellDesigner

A modeling tool for biochemical networks

http://www.celldesigner.org/

Edinburgh Pathway Editor

Edit and draw pathway diagrams

http://epe.sourceforge.net/SourceForge/EPE.html

GenMAPP

Visualization of gene expression and other genomic data on maps representing biological pathways and groupings of genes

http://www.genmapp.org/

Ingenuity IPA

Data integration platform and manually annotated pathways

http://tinyurl.com/IngenuityPath

JDesigner

Graphical modeling environment for biochemical reaction networks

http://jdesigner.sourceforge.net/Site/JDesigner.html

KaPPA View

Plant pathways

http://kpv.kazusa.or.jp/

KEGG Atlas

Interactive Kyoto Encyclopedia of Genes and Genomes pathways

http://www.genome.jp/kegg/

Omix 

Visualizing multi-omics data in metabolic networks

https://www.omix-visualization.com

PathVisio 

Biological pathway analysis software that allows drawing, editing and analysis of biological pathways

http://www.pathvisio.org/

VitaPad 

Application to visualize biological pathways and map experimental data to them

http://tinyurl.com/vitapad/

Web tools for pathways

ArrayXPath 

Mapping and visualizing microarray gene-expression data and integrated biological pathway resources using SVG

http://tinyurl.com/ArrayXPath/

GEPAT 

Integrated analysis of transcriptome data in genomic, proteomic and metabolic contexts

http://gepat.sourceforge.net/

iPath 

Web-based tool for the visualization, analysis and customization of pathway maps

http://pathways.embl.de/

Kegg-Based Viewer 

KEGG-based pathway visualization tool for complex high-throughput data

http://www.g-language.org/data/marray/

MapMan 

User-driven tool that displays large datasets onto diagrams of metabolic pathways or other processes

http://mapman.gabipd.org/web/guest/mapman

MetPA 

Analysis and visualization of metabolomic data within the biological context of metabolic pathways

http://metpa.metabolomics.ca

Omics Viewer 

Data mapping on BioCyc pathways (collection of 5500 pathway/genome databases)

http://www.biocyc.org/

Pathway Explorer

Interactive Java drawing tool for the construction of biological pathway diagrams in a visual way and the annotation of the components and interactions between them

http://genome.tugraz.at/pathwayexplorer/pathwayexplorer_description.shtml

Pathway projector 

Zoomable pathway browser using KEGG atlas and Google Maps API

http://www.g-language.org/PathwayProjector/

PATIKA 

Integrated environment composed of a central database and a visual editor, built around an extensive ontology and an integration framework

http://www.cs.bilkent.edu.tr/~patikaweb/

Reactome SkyPainter 

Visualization of over-represented pathways and reactions from gene lists

http://www.reactome.org/skypainter-2

WikiPathways

Wiki-based, open, public platform dedicated to the curation of biological pathways by and for the scientific community

http://www.wikipathways.org/

]]> Neel https://bioinformaticsonline.com/pages/view/35144/converting-fastq-to-fasta Fri, 12 Jan 2018 03:49:09 -0600 https://bioinformaticsonline.com/pages/view/35144/converting-fastq-to-fasta <![CDATA[Converting FASTQ to FASTA]]>

There are several ways you can convert fastq to fasta sequences. Some methods are listed below.

Using SED

sed can be used to selectively print the desired lines from a file, so if you print the first and 2rd line of every 4 lines, you get the sequence header and sequence needed for fasta format.

sed -n '1~4s/^@/>/p;2~4p' INFILE.fastq > OUTFILE.fasta

Using PASTE

You can linerize every 4 lines in a tabular format and print first and second field using paste

cat INFILE.fastq | paste - - - - |cut -f 1, 2| sed 's/@/>/'g | tr -s "/t" "/n" > OUTFILE.fasta

EMBOSS:seqret

Standard script that can be used for many purposes. One such use is fastq-fasta conversion

seqret -sequence reads.fastq -outseq reads.fasta

awk can be used for conversion as follows:

Using AWK

cat infile.fq | awk '{if(NR%4==1) {printf(">%s\n",substr($0,2));} else if(NR%4==2) print;}' > file.fa

FASTX-toolkit

fastq_to_fasta is available in the FASTX-toolkit that scales really well with the huge datasets

fastq_to_fasta -h
usage: fastq_to_fasta [-h] [-r] [-n] [-v] [-z] [-i INFILE] [-o OUTFILE]
# Remember to use -Q33 for illumina reads!
version 0.0.6
       [-h]         = This helpful help screen.
       [-r]         = Rename sequence identifiers to numbers.
       [-n]         = keep sequences with unknown (N) nucleotides.
                   Default is to discard such sequences.
       [-v]         = Verbose - report number of sequences.
                   If [-o] is specified,  report will be printed to STDOUT.
                   If [-o] is not specified (and output goes to STDOUT),
                   report will be printed to STDERR.
       [-z]         = Compress output with GZIP.
       [-i INFILE]  = FASTA/Q input file. default is STDIN.
       [-o OUTFILE] = FASTA output file. default is STDOUT.

Bioawk

Another option to convert fastq to fasta format using bioawk

bioawk -c fastx '{print ">"$name"\n"$seq}' input.fastq > output.fasta

Seqtk

From the same developer, there is another option using a tool called seqtk

seqtk seq -a input.fastq > output.fasta

Note that you can use either compressed or uncompressed files for this tool

]]> Neel https://bioinformaticsonline.com/pages/view/34552/edit-distance-application-in-bioinformatics Thu, 07 Dec 2017 08:46:51 -0600 https://bioinformaticsonline.com/pages/view/34552/edit-distance-application-in-bioinformatics <![CDATA[Edit distance application in bioinformatics !]]> There are other popular measures of edit distance, which are calculated using a different set of allowable edit operations. For instance,

 

use Text::Levenshtein qw(distance);

 print distance("foo","four");
 # prints "2"

 my @words     = qw/ four foo bar /;
 my @distances = distance("foo",@words);

 print "@distances";
 # prints "2 0 3"


use Algorithm::LCSS qw( LCSS CSS CSS_Sorted );
    my $lcss_ary_ref = LCSS( \@SEQ1, \@SEQ2 );  # ref to array
    my $lcss_string  = LCSS( $STR1, $STR2 );    # string
    my $css_ary_ref = CSS( \@SEQ1, \@SEQ2 );    # ref to array of arrays
    my $css_str_ref = CSS( $STR1, $STR2 );      # ref to array of strings
    my $css_ary_ref = CSS_Sorted( \@SEQ1, \@SEQ2 );  # ref to array of arrays
    my $css_str_ref = CSS_Sorted( $STR1, $STR2 );    # ref to array of strings



There are many different modules on CPAN for calculating the edit distance between two strings. Here's just a selection.

Text::LevenshteinXS and Text::Levenshtein::XS are both versions of the Levenshtein algorithm that require a C compiler, but will be a lot faster than this module.

The Damerau-Levenshtein edit distance is like the Levenshtein distance, but in addition to insertion, deletion and substitution, it also considers the transposition of two adjacent characters to be a single edit. The module Text::Levenshtein::Damerau defaults to using a pure perl implementation, but if you've installed Text::Levenshtein::Damerau::XS then it will be a lot quicker.

Text::WagnerFischer is an implementation of the Wagner-Fischer edit distance, which is similar to the Levenshtein, but applies different weights to each edit type.

Text::Brew is an implementation of the Brew edit distance, which is another algorithm based on edit weights.

Text::Fuzzy provides a number of operations for partial or fuzzy matching of text based on edit distance. Text::Fuzzy::PP is a pure perl implementation of the same interface.

String::Similarity takes two strings and returns a value between 0 (meaning entirely different) and 1 (meaning identical). Apparently based on edit distance.

Text::Dice calculates Dice's coefficient for two strings. This formula was originally developed to measure the similarity of two different populations in ecological research.

 
]]> Neel https://bioinformaticsonline.com/pages/view/33837/awesome-perl-frameworks-libraries-and-software-part-1 Fri, 07 Jul 2017 04:06:59 -0500 https://bioinformaticsonline.com/pages/view/33837/awesome-perl-frameworks-libraries-and-software-part-1 <![CDATA[Awesome perl frameworks, libraries and software - PART 1]]> A curated list of awesome Perl frameworks, libraries and software.

]]> Neel https://bioinformaticsonline.com/pages/view/33486/quick-next-generation-sequencing-ngs-terms-definition Fri, 09 Jun 2017 04:52:26 -0500 https://bioinformaticsonline.com/pages/view/33486/quick-next-generation-sequencing-ngs-terms-definition <![CDATA[Quick next generation sequencing (NGS) terms definition]]> fragment size: the Illumina WGS protocol generates paired-end reads from both ends of longer fragments. The lengths of these fragments are assumed to be sampled from a normal distribution. Therefore, in the absence of structural variants, mapping locations of the paired ends span within an interval [δmin,δmax]. Most (>90%) of paired-end reads are sampled from no-SV regions, therefore the fragment size distribution can be learned empirically for each WGS data set separately.

concordant reads: a read pair is called concordant if they can be mapped to the reference genome as “expected”: (a) mapped to opposing strands where the upstream read is mapped to the forward strand and the downstream read is mapped to the reverse strand2, (b) the distance between ends is between the minimum and maximum expected fragment size.

discordant reads: briefly, any non-concordant read pair is considered discordant. Note that, by definition, the discordant read pairs signal potential SVs. The sequence signature produced by these type of reads is known as read-pair signature.

split reads: a read that can only be mapped to the reference genome by breaking into two sub-reads is called a split-read. These types of reads also indicate a potential SV or a short insertion or deletion (indel).

read depth: number of reads that map within a region of the genome. Overall genome-wide read depth is also referred to as depth of coverage. It is expected that the number of reads that “cover” each base-pair to follow a Poisson distribution. Therefore, if the read depth over a certain region deviates significantly from this distribution, it signals for a potential copy number variation (CNV).

]]> Neel