As Dennis Ritchie writes in his paper, "The Development of the C Language",
C is quirky, flawed, and an enormous success. While accidents of history surely helped, it evidently satisfied a need for a system implementation language efficient enough to displace assembly language, yet sufficiently abstract and fluent to describe algorithms and interactions in a wide variety of environments.

C++ has its basis in C - extending it by supporting features meant to encourage and support the development of large programs. Perhaps most importantly, it supports object-oriented programming in a familiar setting and framework (that of C). When C++ was created, one of the initial aims was to retain compatibility with C to as large an extent as possible, and retain its spirit and efficiency. It was possible to convert from C to C++ gradually, thus making use of C++ (initally, at least) as a "better C", and moving on to using other features. This allowed many C programmers to learn C++ quickly (though using C++ effectively requires a major mind-shift for many C programmers)
Are you really interested in C/C++ language for the biological programming? If yes there is good news for you. Bio++ 1.9.0 is available with amazing libraries that can help you to solve approximately all problems related with biology.

Some of the new feature has been added in the latest version, these are as follows:

Support for codon models (including non-homogenous models),
Tools for manipulating Hidden Markov Models,
Improved numerical tools (numerical derivatives, parameter transforms...),
A new library, Bio++ RAA (Remote Acnuc Access), allowing you to fetch public databases like GenBank, EMBL or SwissProt,
Algorithms for plotting trees, with support for vector formats like SVG, Fig or LaTeX-PGF.
So get relax and solve the HMM problems with an ease with Bio++. J
Now the time has been change, the biological programmers are ready to use the C++ libraries of biology. These library are designed in order to reduce the C++ long codes in a small and handy for the biological programmers. Basically, Bio++ is a set of C++ libraries for Bioinformatics, including sequence analysis,phylogenetics, molecular evolution and population genetics.
Bio++ is designed in an extensible object-oriented way, in the C++ language.

Some of the unique features of the libraries are as follows:
Sequence analysis

Sequence and Site objects, with various Alphabet support (DNA, RNA, Proteins, Codons, any 'Word' of a given size).
Several containers available for inner storage, with several implementations. Support for alignments.
Various I/O formats supported: Fasta, Mase, CLustal, Phylip, DCSE, GenBank (sequence only).
Sequence manipulation: truncation, concatenation, sub-sequences, etc.
In silico molecular biology: (reverse) transcription, translation, replication.
Several genetic codes availables: Standard and mitochondrial (vertebrates, echinoderms and other invertabrates)
Amino acids properties: volume, polarity and charge + physico-chemical distance (Miyata and Grantham) + import from any AAIndex entry.
Consensus sequences.
Pairwise alignment.
Similarity score computation.
Sequence bootstrap.
Homogeneity test (Bowker's test).
etc.

Phylogenetics and molecular evolution 
Data structure and IO

Phylogenetic trees.
IO from newick files, with support for multiple entries.

Phylogenetic reconstuction methods

Parsimony (NNI)
Distance matrices estimation and I/O to files in Phylip format.
Distance methods: (U/W)PGMA, NJ, BioNJ.
Maximum likelihood (NNI, including a PhyML-like algorithm).
Mixed distance/ML tree reconstruction (iterative approaches).
Tree consensus methods, bipartitions, bootstrap value computations.

Substitution models

JC, K80, T92, F84, HKY85, TN93, GTR and more for nucleotides,
JC, DSO78, JTT92 + any PAML-formated model description for proteins, with possibility to estimate equilibrium frequencies.
Various codon models: Muse & Gaut 1994, Yang & Nielsen 1998, Goldman & Yang 1994 + user-defined.
Support for rate-across sites models, with virtually any probability distribution, allowing for invariant classes.
Covarion models.
Model including gaps.
Global clock tree likelihood models.
Virtually any kind of non-homogeneous model is supported!
Mixed models (beta).

Molecular evolution tools

Parameter estimation under maximum likelihood.
Ancestral states reconstructions: Marginal likelihood.
(Weighted) substitution mapping.
Sequences simulation under any substitution model, homogeneous or not.

Population genetics

A new file format to deal with codominant markers and bio-sequence data for individuals.
Import and export methods with various population genetics software.
Specific containers for polymorphism data.
Diversity and polymorphism statistics for codominant and sequence data.
Estimation of Wright F-statistics and pairwise genetic distance on codominant markers.
Statistics on synonymous and non synonymous sites for coding sequences
Various 'Neutrality' statistics on sequence data (Tajima, Fu and Li, Rand and Kann ...).
Various measures of linkage disequilibrium.
etc.

Numerical calculus

Numerical tools: extended functions (log, factorial, etc.)
Vector tools: element-wise functions, statistics (mean, var, sd, correlation, information theory)
Classes for matrices implementation.
Linear algebra: eigen decomposition, LU decomposition, inversion, etc.
Random number generation: Quick & Dirty (32bits only), Wichmann and Hill, Knuth. Samplers from probability distributions (uniform, normal, gamma, etc.).
Function object implementation, with first and second order derivatives.
Numerical derivatives computation.
Optimization algorithms: Golden section search, Brent's algorithm, Powell's and Downhill simplex method, but also methods using derivatives like conjugate gradient and Newton's method. Object implementation of these methods, using the event-driven Optmizer interface (works with Function objects).
Statistics: DataTable object, with I/O from CSV files, probability distributions.
etc.

Utils

Files: working on file paths, getting file extensions and names, testing existence, open and store in string arrays, etc.
Text: convert text to any other type and vice versa, remove spaces, tokenize, switch between upper/lower case, etc.
Applications: read options from a file or command line
etc.

Some of the libraries are under development that will be updated by Bio++ developers on there websites.
C/C++ Tutorial
http://www.cbcb.umd.edu/~jeallen/bioinfo/
Tutorial on Bio++
http://162.38.181.25/BioPP/articles/tutorial/index.html
Download Links
http://162.38.181.25/BioPP/articles/download/index.html

Reference:
http://162.38.181.25/BioPP/index.html
Dutheil J, Boussau B. Non-homogeneous models of sequence evolution in the Bio++ suite of libraries and programs. BMC Evol Biol. 2008 Sep 22;8(1):255
Dutheil J, Gaillard S, Bazin E, Glémin S, Ranwez V, Galtier N, Belkhir K. Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics. BMC Bioinformatics. 2006 Apr 4;7:188.
Dutheil JY, Ganapathy G, Hobolth A, Mailund T, Uyenoyama MK, Schierup MH. Ancestral population genomics: the coalescent hidden Markov model approach. Genetics. 2009 Sep;183(1):259-74.
Nabholz B, Mauffrey J-F, Bazin E, Galtier N, Glémin S. Determination of Mitochondrial Genetic Diversity in Mammals. Genetics. 2008 January; 178(1): 351-361.
Galtier N. A model of horizontal gene transfer and the bacterial phylogeny problem. Syst Biol. 2007 Aug;56(4):633-42.
Dutheil J, Galtier N. Detecting groups of coevolving positions in a molecule: a clustering approach. BMC Evol Biol. 2007; 7: 242.
Boussau B, Gouy M. Efficient likelihood computations with nonreversible models of evolution. Syst Biol. 2006 Oct;55(5):756-68.
Dutheil J, Pupko T, Jean-Marie A, Galtier N. A model-based approach for detecting coevolving positions in a molecule. Mol Biol Evol. 2005 Sep;22(9):1919-28.

The GO provides core biological knowledge representation for modern biologists, whether computationally or experimentally based. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Although extensively used in data analysis workflows, and widely incorporated into numerous data analysis platforms and applications, the general user of GO resources often misses fundamental distinctions about GO structures, GO annotations, and what can and can not be extrapolated from GO resources. Here are ten quick tips for using the Gene Ontology.

Read "Ten Quick Tips for Using the Gene Ontology" at http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003343

Following are the most commonly used old and new GO term enrichment determination tools. These tools are recommended to people working in a wet-lab.

CLASSIFI (Department of Pathology, UT Southwestern Medical Center)

CLASSIFI (Cluster Assignment for Biological Inference) is a data-mining tool that can be used to identify significant co-clustering of genes with similar functional properties (e.g. cellular response to DNA damage). Briefly, CLASSIFI uses the Gene OntologyTM (GO) gene annotation scheme to define the functional properties of all genes/probes in a microarray data set, and then applies a cumulative hypergeometric distribution analysis to determine if any statistically significant gene ontology co-clustering has occurred.

http://pathcuric1.swmed.edu/pathdb/classifi.html

EasyGO (China Agricultural University)

EasyGO is designed to automate enrichment job for experimental biologists to identify enriched Gene Ontology (GO) terms in a list of microarray probe sets or gene identifiers (with expression information for PAGE analysis). Also EasyGO is also a GO annotation database, especially focus on agronomical species, supporting 30 species. It is user friendly, with advanced result browsing format and in-time update.

http://bioinformatics.cau.edu.cn/neweasygo/

http://bioinformatics.cau.edu.cn/easygo/

g:GOSt (Institute of Computer Science, University of Tartu)

g:GOSt retrieves most significant Gene Ontology (GO) terms, KEGG and REACTOME pathways, and TRANSFAC motifs to a user-specified group of genes, proteins or microarray probes. g:GOSt also allows analysis of ranked or ordered lists of genes, visual browsing of GO graph structure, interactive visualisation of retrieved results, and many other features. Multiple testing corrections are applied to extract only statistically important results.

http://biit.cs.ut.ee/gprofiler/

DAVID : Gene Functional Classification (Laboratory of Immunopathogenesis and Bioinformatics, NIAID)

The Functional Classification Tool provides a rapid means to organize large lists of genes into functionally related groups to help unravel the biological content captured by high throughput technologies.

http://david.abcc.ncifcrf.gov/gene2gene.jsp

http://david.abcc.ncifcrf.gov/

API https://github.com/chrisamiller/davidapi

GOEAST (Institute of Genetics and Developmental Biology, Chinese Academy of Sciences)

GOEAST is web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods.

http://omicslab.genetics.ac.cn/GOEAST/

GOstat (Walter and Eliza Hall Institute of Medical Research)

Find statistically overrepresented GO terms within a group of genes

http://gostat.wehi.edu.au/

GOrilla (Technion - Laboratory of Computational Biology , Israel Institute of Technology)

GOrilla is a tool for identifying and visualizing enriched GO terms in ranked lists of genes.
It uses two approaches, first by searching for enriched GO terms that appear densely at the top of a ranked list of genes  or by searching for enriched GO terms in a target list of genes compared to a background list of genes.

GOrilla makes nice pictures !!!!

http://cbl-gorilla.cs.technion.ac.il/

Gene Ontology for Functional Analysis (GOFFA)

GOFFA is a tool developed for ArrayTrack™ that takes a list of genes and identifies terms in Gene Ontology (GO) disclaimer icon associated with those genes.

It provides several tools to view/access the GO term hierarchy, full listing of GO terms annotated with the genes associated with a given term with statically useful report.

http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm233315.htm

GOAT (The University of Manchester)

The aim of the GOAT project is to create an application that will guide users, especially biomedical researchers, in the annotation of gene products with terms from the Gene Ontology.

http://goat.man.ac.uk/

Script https://github.com/tanghaibao/goatools/

REVIGO ( Rudjer Boskovic Institute, Croatia)

REViGO is a web server that can take long lists of Gene Ontology terms and summarize them by removing redundant GO terms. The remaining terms can be visualized in semantic similarity-based scatterplots, interactive graphs, or tag clouds.

http://revigo.irb.hr/

QuickGo (EMBL-EBI Institute)

It uses extensive computational filters to allow the generation of specific subsets of GO annotations, mapped to sequence identifiers of your choice. Then GO slims are used which is collective list of GO full set of terms available from the Gene Ontology project.

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

GOLEM

An interactive graph-based gene-ontology navigation and analysis tool. GOLEM is a userful tool which allows the viewer to navigate and explore a local portion of the Gene Ontology (GO) hierarchy.

http://reducio.princeton.edu/GOLEM/

BGI Web Gene Ontology (WEGO) Annotation Plot (Beijing Genomics Institute)

WEGO () is a useful tool for plotting GO annotation results. It has been widely used in many important biological research projects, such as the rice genome project [Yu, J. et al. Science 296, 79-92 (2002); Yu, J. et al. PLoS Biol 3, e38 (2005)] and the silkworm genome project [Xia, Q. et al. Science 306, 1937-40 (2004)]. It has become one of the daily tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. WEGO along with two other tools, namely External to GO Query and GO Archive Query, are freely available for all users. Any suggestions are welcome at wego@genomics.org.cn. Here is a sample output generated by WEGO

http://wego.genomics.org.cn/cgi-bin/wego/index.pl

GeneGO MetaCore (MIT)

GeneGo is a leading provider of data mining & analysis solutions in systems biology. MetaCore, GeneGo's flapship product, is an integrated software suite for functional analysis of experimental data. MetaCore is based on a curated database of human protein-protein, protein-DNA interactions, transcription factors, signaling and metabolic pathways, disease and toxicity, and the effects of bioactive molecules.

https://portal.genego.com/

GOEx (Stony Brook University)

GOEx facilitates organism-specific studies by leveraging GO and providing a rich graphical user interface. It is a simple to use tool, specialized for biologists who wish to analyze spectral counting data from shotgun proteomics.

http://pcarvalho.com/patternlab

GOssTo

GOssTo and GOssToWeb are tools to calculate the semantic similarity between genes or terms in the Gene Ontology.

http://www.paccanarolab.org/gosstoweb/

GO Workbench

The Gene Ontology Analysis Viewer allows direct browsing of the Gene Ontology, and also the visualization of GO Term analysis results.

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

Some other useful list of GO software and tools is available at http://www.geneontology.org/GO.tools.shtml#browser

Yet another useful webpage with list of GO tools at http://neurolex.org/wiki/Category:Resource:Gene_Ontology_Tools

It was first developed to transfer annotations between different genome assembly versions. However, it can also transfer annotations between strains and even different species, like Plasmodium chabaudi onto P. berghei, between different Leishmania species or Salmonella enterica onto other Salmonella serotypes. RATT is able to transfer any entries present on a reference sequence, such as the systematic id or an annotator's notes; such information would be lost in a de novo annotation.

More at http://ratt.sourceforge.net/

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

HiCdat is easy-to-use and provides solutions starting from aligned reads up to in-depth analyses. Importantly, HiCdat is focussed on the analysis of larger structural features of chromosomes, their correlation to genomic and epigenomic features, and on comparative studies. It uses simple input and output formats and can therefore easily be integrated into existing workflows or combined with alternative tools.

More at http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0678-x

Address of the bookmark: https://github.com/MWSchmid/HiCdat

More at https://sourceforge.net/projects/sb2nhri/files/MyPro/

Address of the bookmark: http://www.sciencedirect.com/science/article/pii/S0167701215001207

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/

Address of the bookmark: http://pasapipeline.github.io/

Some of the prominent features of the package are:

• visualizing polyploidy simultaneously on the same plot.
• annotating groups of elements as distinct colors.
• creating chromosome heatmaps.
• adjusting chromosome range or visualizing chromosome regions such as genes
• adding labels to the plot
• adding hyperlinks to each element

Address of the bookmark: https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html

Address of the bookmark: http://genomearchitect.github.io/

The bpRNA code is written in perl and requires the Graph perl module. Several additional scripts for analysis are included. The source code is available at http://github.com/hendrixlab/bpRNA.

Address of the bookmark: http://github.com/hendrixlab/bpRNA