Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.

Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.

Address of the bookmark: https://github.com/lh3/wgsim

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2014/02/12/bioinformatics.btu078.full.pdf

Using the reference sequence dictionary (*.dict), it also creates some empty BAM files if no sam record was found for a chromosome. A pair of 'mock' SAM-Records can also be added to those empty BAMs to avoid some tools (like samtools) to crash.

Usage

java -jar splitbam.jar -p OUT/__CHROM__/__CHROM__.bam -R ref.fasta (bam|sam|stdin)

Options

• -h help; This screen.
• -R (indexed reference file) REQUIRED.
• -u (unmapped chromosome name): default:Unmapped
• -e | --empty : generate EMPTY bams for chromosome having no read mapped
• -m | --mock : if option '-e', add a mock pair of sam records to the empty bam
• -p (output file/bam pattern) REQUIRED. MUST contain __CHROM__ and end with .bam
• -s assume input is sorted.
• -x | --index create index.
• -t | --tmp (dir) tmp file directory
• -G (file) chrom-group file (see below)

Address of the bookmark: https://code.google.com/archive/p/jvarkit/wikis/SplitBam.wiki

Address of the bookmark: https://github.com/marbl/Krona/wiki

QuorUM is distributed as an independent software package and as a module of the MaSuRCA assembly software. Both are available under the GPL open source license at http://www.genome.umd.edu.

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130821

HECIL’s core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.

Address of the bookmark: https://github.com/NDBL/HECIL

What is de novo genome assembly?
The method of taking a large number of short DNA sequences and placing them back together to create a reflection of the original chromosomes from which the DNA originated relates to genome assembly. No previous knowledge of the source DNA sequence length, structure or composition is inferred by De novo genome assemblies. The DNA of the target organism is split up into millions of tiny parts and read on a sequencing computer in a genome sequencing experiment. Depending on the sequencing system used, these "reads" range from 20 to 1000 nucleotide base pairs (bp) in length. Usually, length reads of 36 - 150 bp are produced for Illumina style short read sequencing. These reads can be either “single ended” as described above or “paired end.”

Why genome assembly?
In basic research into why and how they live, as well as in applied topics, identifying the DNA sequence of an organism is useful. Awareness of a DNA sequence may be useful in virtually any biological research because of the relevance of DNA to living things. For example, it may be used in medicine to classify, diagnose and eventually improve genetic disorder therapies. Similarly, pathogens study can lead to treatments for infectious diseases.

Raw NGS data
Reads can be saved as a Fasta file as text or in a FastQ file with their attributes. FastQ is the most common read file format since this is what the Illumina sequencing pipeline creates. This will henceforth be the subject of our conversation.

In a nutshell the protocol:
Get the sequence file(s) read from the sequencing machine (s).
Look at the readings - have an idea of what you have and what the standard is like.
If required, raw data cleanup/quality trimming.
Choose an adequate parameter set for assembly.
Assemble the data into scaffolds/contigs.
Examine the assembly performance and determine the efficiency of the assembly.

Read Quality Control:
Check the qualiy with fastQC.
Script
https://bioinformaticsonline.com/snippets/view/42540/install-fastqc-using-conda

Quality trimming/cleanup of read files.
This function trims adapters, barcodes and other contaminants from the reads.
Script
https://bioinformaticsonline.com/snippets/view/42542/trimmomatic-command

Genome Assembly:
The object of this portion of the protocol is to explain the method of assembling the reads trimmed by quality into draft contigs.

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o result_of_spades_assembly_all_illumina

A significant range of short-read assemblers are available. Everyone with strengths and disadvantages of their own.
Some of the assemblers available include:
Velvet
SOAP-denovo
MIRA
ALLPATHS

Next step is to assess the suitability and what to do with a draft package of contiguous details for the remainder of the study now. Few stuff you can note about the contigs you just created: They're the draft Contigs. Any mis-assemblies can occur.

Mis-assembly checking and assembly metric tools:
QUAST - Quality assessment tool for genome assembly http://bioinf.spbau.ru/quast
Mauve assembly metrics - http://code.google.com/p/ngopt/wiki/How_To_Score_Genome_Assemblies_with_Mauve
InGAP-SV - https://sites.google.com/site/nextgengenomics/ingap and http://ingap.sourceforge.net/
inGAP is also useful for finding structural variants between genomes from read mappings.

Genome finishing tools:
Semi-automated gap fillers:
Gap filler - http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/gapfiller/

IMAGE (V2) - http://sourceforge.net/apps/mediawiki/image2/index.php?title=Main_Page

Genome visualisers and editors:
Artemis - http://www.sanger.ac.uk/resources/software/artemis/
IGV - http://www.broadinstitute.org/igv/

Automated and semi automated annotation tools:
Prokka - https://github.com/tseemann/prokka
RAST - http://www.nmpdr.org/FIG/wiki/view.cgi/FIG/RapidAnnotationServer
JCVI Annotation Service - http://www.jcvi.org/cms/research/projects/annotation-service/

Frequent command use for the analysis are at:

https://bioinformaticsonline.com/blog/view/38765/list-of-tools-frequently-used-while-genome-assembly
https://bioinformaticsonline.com/pages/view/42275/frequent-parameters-for-bioinformatics-tools

Before you go down that path, consider this critical biological question:
Are you sequencing human cells—or bacterial contamination?

The Silent Saboteur: Mycoplasma in Cell Cultures

Mycoplasma contamination remains one of the most widespread and underdiagnosed issues in tissue culture work. Studies suggest that 15–35% of cell lines in use may be contaminated, often without visible signs. Unlike other microbial infections, Mycoplasma does not produce cloudiness, odor, or a change in pH. Many researchers won’t detect it unless they specifically test for it.

The consequences, however, are profound. Mycoplasma can significantly alter:

• Host gene expression patterns

• Cell proliferation rates

• Epigenetic profiles and chromatin accessibility

• Cytokine signaling and immune responses

In short, it can skew your results, compromise your biological conclusions, and invalidate weeks or months of research.

A Simple Diagnostic Step: Map Against Mycoplasma Genomes

If you encounter poor alignment rates to the human genome, consider mapping your reads to a Mycoplasma reference genome—or better yet, use a combined human + Mycoplasma reference. There have been cases where over half of all reads, initially assumed to be from human cells, were in fact bacterial in origin. This check is fast, easy, and could save your project.

How Contamination Happens—and Persists

Mycoplasma is small (0.1–0.3 μm), lacks a cell wall, and can pass through standard filters undetected. Common sources include:

• Contaminated reagents (e.g., FBS)

• Infected cell lines obtained from other labs

• Poor aseptic technique or shared equipment

Once present, it spreads quickly between cultures and can persist for months, silently affecting results.

Why Treatment Is Difficult

While antibiotics such as Plasmocin or BM-Cyclin are sometimes used, they often offer only partial resolution and may themselves alter cell behavior. In many cases, the best course of action is to discard the contaminated culture and start with a fresh, verified stock.

Practical Recommendations for Researchers

• Routinely test for Mycoplasma using PCR, qPCR, or fluorescence-based assays

• Incorporate contamination screens into your sequencing QC pipeline

• Use combined reference genomes when mapping ambiguous reads

• Practice strict aseptic technique and monitor all incoming cell lines

• Don’t ignore unexplained data anomalies—they might point to contamination

Closing Thought: Contamination Is a Biological Variable

It’s easy to view poor mapping as a technical issue, but sometimes the problem lies deeper—in the biology itself. Mycoplasma contamination doesn’t just interfere with sequencing; it interferes with science. As a research community, we must treat contamination not as an afterthought, but as a key variable to control.

So next time your reads won’t align, don’t just tune the aligner. Ask if your cells are telling the truth—or if they're hiding something.

.NET Bio

http://blogs.msdn.com/b/msr_er/archive/2011/10/18/microsoft-biology-foundation-evolves-into-new-toolkit-net-bio.aspx

A language-neutral bioinformatics toolkit built using the Microsoft 4.0 .NET Framework to help developers, researchers, and scientists.

AMPHORA ("AutoMated Phylogenomic infeRence Application")

http://wolbachia.biology.virginia.edu/WuLab/Software.html

Metagenomics analysis software

Anduril

http://www.anduril.org/anduril/site/

Component-based workflow framework for data analysis

Armadillo workflow platform

Tool for designing and executing phylogenetic workflows

AutoDock

http://autodock.scripps.edu/

suite of automated docking tools

Biochemical Algorithms Library (BALL)

http://www.ball-project.org/

C++ library and framework for molecular modeling and visualization designed for rapid prototyping

Bio4j

http://bio4j.com/

Bio4j is a bioinformatics platform and graph based database built around most data available in UniProt KB(Swiss-Prot + TrEMBL), Gene Ontology (GO), UniRef (50,90,100), RefSeq, NCBI taxonomy, and Expasy Enzyme DB

Bioclipse

www.bioclipse.net

Visual platform for chemo- and bioinformatics based on the Eclipse Rich Client Platform (RCP).

Bioconductor

http://www.bioconductor.org/

R (programming language) language toolkit

Bioinformatics Learning Tutorial (BLT)

http://sourceforge.net/projects/biotutorial/

Educational interactive tutorials and 3D animations for Replication, Transcription, and Translation

BioHaskell

http://biohaskell.org/

Haskell (programming language)

BioJava

http://biojava.org/wiki/Main_Page

Java (programming language)

BioMOBY

http://biomoby.org/

registry of web services

BioPerl

http://www.bioperl.org/wiki/Main_Page

Perl language toolkit

BioPHP

http://www.biophp.org/

PHP language toolkit

Biopython

http://biopython.org/wiki/Main_Page

Python language toolkit

BioRails

https://github.com/biorails

a data management system designed to support researchers in drug discovery

BioRuby

http://bioruby.org/

Ruby language toolkit

BioSmalltalk

https://code.google.com/p/biosmalltalk/

Smalltalk language toolkit

BioUno

http://www.biouno.org/

BioUno is a project that applies Continuous Integration tools and techniques in Bioinformatics. It uses Jenkins and its plug-in API to create biology workflows and manage computer clusters.

caCORE

ontologic representation environment

caArray

https://cabig-stage.nci.nih.gov/community/tools/caArray

ontologic representation environment

EMBOSS

http://emboss.sourceforge.net/

Suite of packages for sequencing, searching, etc.

Gaggle

https://www.gaggle.net/

A framework for interoperability between systems biology software

Galaxy

http://galaxyproject.org/

Scientific workflow and data integration system

GenePattern

http://www.broadinstitute.org/cancer/software/genepattern/

Scientific workflow system that provides access to more than 150 genomic analysis tools

GeWorkbench

http://wiki.c2b2.columbia.edu/workbench/index.php/Home

Genomic data integration platform

GMOD

http://www.gmod.org/wiki/Main_Page

Toolkit for addressing many common challenges at biological databases.

GeneProf

http://www.geneprof.org/GeneProf/

A web-based, bioinformatics software suite for the analysis of functional genomics experiments, e.g. RNA-seq or ChIP-seq.

GeneTalk

http://www.gene-talk.de/

Tool for filtering sequence variants in VCF files. Network for scientists and clinicians for expertise and knowledge exchange. Database of annotations aboute sequence variants with clinically relevant information.

GenGIS

http://kiwi.cs.dal.ca/GenGIS/Main_Page

Application that allows users to combine digital map data with information about biological sequences collected from the environment.

GenomeSpace

http://www.genomespace.org/

Centralized web application that provides data format transformations and facilitates connections with other bioinformatics tools

GENtle

http://directory.fsf.org/wiki/GENtle

An equivalent to the proprietary Vector NTI, a tool to analyze and edit DNA sequence files

Integrated Genome Browser

http://bioviz.org/igb/

Java-based desktop genome browser

Integrative Genomics Viewer (IGV)

http://www.broadinstitute.org/igv/

High-performance desktop tool for interactive visual exploration of diverse genomic data

IntAct

http://www.ebi.ac.uk/intact/

molecular interaction database

InterMine

http://intermine.github.io/intermine.org/

Extensive data warehouse system for the analysis and integration of biological datasets

Java Treeview

http://jtreeview.sourceforge.net/

microarray data viewer

LabKey Server

http://labkey.com/

platform for integrating, analyzing and sharing data

OpenClinica

https://www.openclinica.com/

software for capturing and managing data in clinical trials

PromKappa

http://xbioinformatics.wordpress.com/tag/promkappa/

PromKappa (Promoter analysis by Kappa) software program used for promoter pattern generation and promoter analysis.

MeV: Multi-Experiment Viewer

http://www.tm4.org/mev.html

a desktop application for the analysis, visualization and data-mining of large-scale genomic data

PathVisio

http://www.pathvisio.org/

a desktop software for drawing, analysis and visualization of biological pathways

REDCRAFT

software for determining tertiary protein structure given assigned Residual Dipolar Coupling data

SAM Tools

Data format (SAM) and accompanying tool suite, for storing large nucleotide sequence alignments

Staden Package

Sequence assembly, editing and analysis, primarily consisting of gap4, gap5 and spin.

STAMP

Software package for analyzing metagenomic profiles that promotes ‘best practices’ in choosing appropriate statistical techniques and reporting results.

supraHex

An open-source R/Bioconductor package for omics data analysis using a supra-hexagonal map

Taverna workbench

Tool for designing and executing workflows

TGAC Browser

Genome Browser, visualisation solutions for big data in the genomic era

T-REX WebServer

Bioinformatics and phylogenetics webserver (NJ, PhyML, RAxML, MAFFT, MUSCLE, Newick viewer, Horizontal gene transfer detection, Reticulograms, Substitution models)

UGENE

integrated bioinformatics tools

Visomics

bioinformatics tools for omics data

Genome Analysis Toolkit 1.0 (GATK 1.0)

a software package to analyse next-generation resequencing data

Address of the bookmark: http://pevsnerlab.kennedykrieger.org/php/?q=software