BOL: Related items

plumber:An R package that converts your existing R code to a web API

BioJoker — Wed, 13 Mar 2019 19:20:10 -0500

plumber allows you to create a REST API by merely decorating your existing R source code with special comments. Take a look at an example.

# plumber.R

#* Echo back the input
#* @param msg The message to echo
#* @get /echo
function(msg=""){
  list(msg = paste0("The message is: '", msg, "'"))
}

#* Plot a histogram
#* @png
#* @get /plot
function(){
  rand <- rnorm(100)
  hist(rand)
}

#* Return the sum of two numbers
#* @param a The first number to add
#* @param b The second number to add
#* @post /sum
function(a, b){
  as.numeric(a) + as.numeric(b)
}

Address of the bookmark: https://www.rplumber.io/

A guide for complete R beginners !

Archana Malhotra — Fri, 20 Feb 2015 23:36:46 -0600

This tutorial is intended to introduce users quickly to the basics of R, focusing on a few common tasks that biologists need to perform some basic analysis: load a table, plot some graphs, and perform some basic statistics. More extensive tutorials can be found on the project website and via bioconductor (not covered here).

R-language: http://www.r-project.org

BioConductor: http://www.bioconductor.org

Advantages of R

Free!
Powerful, many libraries have been created to perform application specific tasks. e.g. analysis of microarray experiments and Next-Gen sequencing (bioconductor: including Bioseq group).
Presentation quality graphics
- Save as a png, pdf or svg
History
- What you do can be saved for the next time you use R.
- Ability to turn it into an automated script to perform again and again on different data

Disadvantages

Lack of a comprehensive graphical user interface, but two do exist: However some do exist: R commander: http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/ and Limma-gui (microarrays) : http://bioinf.wehi.edu.au/limmaGUI/

Preparation

(Optional) Download and save the tutorial data set from
- http://bioinformatics.knowledgeblog.org/wp-content/uploads/bioinf/kerr/data.tsv
- Start R (type R on a Linux or Mac terminal, or find the starting link from PC)

Getting More Help

Project Home page
- http://www.r-project.org/
- Check out the ‘introduction to R’, which is a much more in depth guide .
- Also R has a built-in help system (see later)

Working directory

This is the directory used to store your data and results. It is useful if it is also the directory where your input data is stored.

Mac/Linux: this is the directory where you typed in R
PC: Change using the change working directory option

Next generation sequencing in R or bioconductor environment

John Parker — Mon, 02 Jun 2014 18:03:09 -0500

There are many R software and bioconductor packages for NGS data analysis, some of them are as follows

Biostrings

The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R. It contains many speed and memory effective string containers, string matching algorithms, and other utilities, for fast manipulation of large sets of biological sequences. The objects and functions provided by Biostrings form the basis for many other sequence analysis packages. Documentation

IRanges Overview

IRanges provides the low-level infrastructure and containers for handling sets of integer ranges within Bioconductor's BioC-Seq domain. Its classes and methods provide support for many more high-level packages like GenomicRanges, ShortRead, Rsamtools, etc. Documentation

GenomicRanges Overview

The GenomicRanges package serves as the foundation for representing genomic locations within the Bioconductor project. It is built upon the IRanges infrastructure and defines three major data containers - GRanges, GRangesList and GappedAlignments - which are supporting other important BioC-Seq packages including ShortRead, Rsamtools, rtracklayer, GenomicFeatures and BSgenome. Compared to the IRanges container, the GRanges/GRangesList classes are more flexible and extensible to store additional information about sequence ranges, such as chromosome identifiers (sequence space), strand information and annotation data. Documentation

Motif Discovery

cosmo

The cosmo package allows to search a set of unaligned DNA sequences for a shared motif that may function as transcription factor binding site. The algorithm extends the popular motif discovery tool MEME (Bailey and Elkan, 1995) in that it allows the search to be supervised by specifying a set of constraints that the motif to be discovered must satisfy. Documentation

BCRANK

BCRANK is a method that takes a ranked list of genomic regions as input and outputs short DNA sequences that are overrepresented in some part of the list. The algorithm was developed for detecting transcription factor (TF) binding sites in a large number of enriched regions from high-throughput ChIP-chip or ChIP-seq experiments, but it can be applied to any ranked list of DNA sequences. Documentation

rGADEM: Documentation

MotIV: Documentation

ShortRead

The ShortRead package provides input, quality control, filtering, parsing, and manipulation functionality for short read sequences produced by high throughput sequencing technologies. While support is provided for many sequencing technologies, this package is primairly focused on Solexa/Illumina reads. Documentation

Rsamtools

Rsamtools provides functions for parsing and inspecting samtools BAM formatted binary alignment data. SAM/BAM is quickly becoming a universal standard alignment format, and is now supported by a wide variety of alignment tools. Documentation

Samtools Website
BWA (Burrows-Wheeler Alignment) Website

Additional tools for SNP analysis:

snpMatrix

BSgenome

BSgenome provides an object oriented infrastructure for interacting with a Biostring based genome sequence. BSgenome packages exist for many common genomes, and can be created to represent custom genomes. See the "How to forge a BSgenome data package" Vignette for instructions to create a new BSgenome package if a prebuilt package does not exist for your organism. Documentation

rtracklayer

rtracklayer provides an interface for exporting annotation feature data to various genome browsers and file formats (such as GFF). See the Small RNA Profiling exercise for an example of using rtracklayer to visualize alignment coverage. Documentation

biomaRt

The biomaRt package, provides an interface to a growing collection of databases implementing the BioMart software suite (http:// www.biomart.org). The package enables online retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas. This data is retrieved automatically via the Internet, so it's recommended that you cache the data locally, or check versions if your code will be adversely affected by updates to these data. Documentation

ChIP-Seq Analysis Packages

Bioconductor provides various packages for analyzing and visualizing ChIP-Seq data. Only a small selection of these packages is introduced here. Additional useful introductions to this topic are: BioC ChIP-seq Case Study and BioC ChIP-Seq.

chipseq

The chipseq package combines a variety of HT-Seq packages to a pipeline for ChIP-Seq data analysis. Documentation

BayesPeak

BayesPeak is a peak calling package for identifying DNA binding sites of proteins in ChIP-Seq experiments. Its algorithm uses hidden Markov models (HMM) and Bayesian statistical methods. The following sample code introduces the identification of peaks with the BayesPeak package as well as the incorporation of read coverage information obtained by the chipseq package. Documentation [ Publication ]

PICS

The PICS package applies probabilistic inference to aligned-read ChIP-Seq data in order to identify regions bound by transcription factors. PICS identifies enriched regions by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. The following sample code uses the test data set from the above BayesPeak package in order to compare the results from both methods by identifying their consensus peak set. Documentation [ Publication ]

ChIPpeakAnno

The ChIPpeakAnno package provides. batch annotation of the peaks identified from either ChIP-seq or ChIP-chip experiments. It includes functions to retrieve the sequences around peaks, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. The package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. Documentation

Additional ChIP-Seq Packages

DiffBind: Documentation

MOSAICS: Documentation

iSeq: Documentation

ChIPseqR: Documentation

ChiPsim: Documentation

CSAR: Documentation

ChIP-Seq Pipeline: PICS, rGADEM and MotIV (developer web site)

SPP: ChIP-seq processing pipeline

SPP Tutorial

MACS

SIPeS

RNA-Seq Analysis

Counting Reads that Overlap with Annotation Ranges

The GenomicRanges package provides support for importing into R short read alignment data in BAM format (via Rsamtools) and associating them with genomic feature ranges, such as exons or genes. This way one can quantify the number of reads aligning to annotated genomic regions. The package defines general purpose containers for storing genomic intervals as well as more specialized containers for storing alignments against a reference genome. The two main functions for read counting provided by this infrastructure are countOverlaps and summarizeOverlaps. For their proper usage, it is important to read the corresponding PDF manual. Documentation

Differential Gene Expression Analysis with DESeq

The DESeq package contains functions to call differentially expressed genes (DEGs) in count tables based on a model using the negative binomial distribution. It expects as input a data frame with the raw read counts per region/gene of interest (rows) for each test sample (columns). Such a count table can be imported into R or generated from BAM alignment files using the countOverlaps function as introduced above. Documentation

Differential Gene Expression Analysis with edgeR

The edgeR package uses empirical Bayes estimation and exact tests based on the negative binomial distribution to call differentially expressed genes (DEGs) in count data.

Documentation

A variety of additional R packages are available for normalizing RNA-Seq read count data and identifying differentially expressed genes (DEG):

easyRNASeq (simplifies read counting per genome feature)

DEXSeq (Inference of differential exon usage); parathyroidSE explains how to generate exon read counts in R

DEGseq

baySeq (also see: segmentSeq)

Genominator (Bullard et al. 2010)

Detection of Alternative Splice Junctions

Another utility of RNA-Seq experiments is the analysis of splice junctions. The following software suggestions provide this utility:

ERANGE
TopHat

SpliceMap

SplitSeek

DNA-Methylation Data Analysis

methylPipe
bsseq
BiSeq
Much more under BiocViews

HT-Seq Data Visualization

ggbio: ggplot2 extension for genomics data (online manual) Gviz: Plotting data and annotation information along genomic coordinates HilbertVis: Hilbert genome plots

GenomeGraphs: Plotting genomic information from Ensembl

TileQC: Flow Cell Quality Visualization

rtracklayer: R interface to genome browsers

genoPlotR: Plotting maps of genes and genomes

Genominator: Tools for storing, accessing, analyzing and visualizing genomic data.

To install all packages

source("http://bioconductor.org/biocLite.R")
biocLite()
biocLite(c("ShortRead", "Biostrings", "IRanges", "BSgenome", "rtracklayer", "biomaRt", "chipseq", "ChIPpeakAnno", "Rsamtools", "BayesPeak", "PICS", "GenomicRanges", "DESeq", "edgeR", "leeBamViews", "GenomicFeatures", "BSgenome.Celegans.UCSC.ce2"))

RStudio

Jitendra Narayan — Sat, 27 Dec 2014 06:50:58 -0600

RStudio IDE is a powerful and productive user interface for R. It’s free and open source, and works great on Windows, Mac, and Linux.

The developers and expert trainers are the authors of several popular R packages, including ggplot2, plyr, lubridate, and others.

More at http://www.rstudio.com/

http://www.rstudio.com/products/RStudio/

Address of the bookmark: http://www.rstudio.com/

A guide for complete R beginners :- R Syntax

Archana Malhotra — Fri, 20 Feb 2015 23:41:03 -0600

R is a functional based language, the inputs to a function, including options, are in brackets. Note that all dat and options are separated by a comma

Function(data, options)

Even quit is a function

So is help

help(read.table)

Provides the help page for the FUNCTION ‘read.table’

help.search(“t test”)

Searches for help pages that might relate to the phrase ‘t test’

NOTE: quotes are needed for search strings, they are not needed when referring to data objects or function names.

There is a short cut for help,

? shows the help page on a function name, same as help(function)

?read.table

?? searches for help pages on functions, same as help.search(‘phrase’)

??“t test”

Information is usually returned from a function, by default this is printed to screen

read.table(‘data.tsv’)

This can always be stored, we call what it is stored in an ‘object’

mydata

here mydata is an object of type dataframe

Reminder:

Vector: a list of numbers, equivalent to a column in a table
Data Frame = a collection of vectors. Equivalent to a table

Hint:

Up/Down arrow keys can be use to cycle through previous commands

R 3.2.0 is released

Neel — Sat, 18 Apr 2015 05:06:44 -0500

R 3.2.0 (codename “Full of Ingredients”) was released yesterday. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below.

Upgrading to R 3.2.0 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code:

# installing/loading the latest installr package:
install.packages("installr"); library(installr) #load / install+load installr
 
updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.).

If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

NEW FEATURES

anyNA() gains a recursive argument.
When x is missing and names is not false (including the default value), Sys.getenv(x, names) returns an object of class "Dlist" and hence prints tidily.
(Windows.) shell() no longer consults the environment variable SHELL: too many systems have been encountered where it was set incorrectly (usually to a path where software was compiled, not where it was installed). R_SHELL, the preferred way to select a non-default shell, can be used instead.
Some unusual arguments to embedFonts() can now be specified as character vectors, and the defaults have been changed accordingly.
Functions in the Summary group duplicate less. (PR#15798)
(Unix-alikes.) system(cmd, input = ) now uses ‘shell-execution-environment’ redirection, which will be more natural if cmd is not a single command (but requires a POSIX-compliant shell). (Wish of PR#15508)
read.fwf() and read.DIF() gain a fileEncoding argument, for convenience.
Graphics devices can add attributes to their description in .Device and .Devices. Several of those included with R use a "filepath" attribute.
pmatch() uses hashing in more cases and so is faster at the expense of using more memory. (PR#15697)
pairs() gains new arguments to select sets of variables to be plotted against each other.
file.info(, extra_cols = FALSE) allows a minimal set of columns to be computed on Unix-alikes: on some systems without properly-configured caching this can be significantly faster with large file lists.
New function dir.exists() in package base to test efficiently whether one or more paths exist and are directories.
dput() and friends gain new controls hexNumeric and digits17 which output double and complex quantities as, respectively, binary fractions (exactly, see sprintf("%a")) and as decimals with up to 17 significant digits.
save(), saveRDS() and serialize() now support ascii = NA which writes ASCII files using sprintf("%a") for double/complex quantities. This is read-compatible with ascii = TRUE but avoids binary->decimal->binary conversions with potential loss of precision. Unfortunately the Windows C runtime’s lack of C99 compliance means that the format cannot be read correctly there in R before 3.1.2.
The default for formatC(decimal.mark =) has been changed to be getOption("OutDec"); this makes it more consistent with format() and suitable for use in print methods, e.g. those for classes "density", "ecdf", "stepfun" and "summary.lm".
getOption("OutDec") is now consulted by the print method for class "kmeans", by cut(), dendrogram(), plot.ts() and quantile() when constructing labels and for the report fromlegend(trace = TRUE).

(In part, wish of PR#15819.)
printNum() and hence format() and formatC() give a warning if big.mark and decimal.mark are set to the same value (period and comma are not uncommonly used for each, and this is a check that conventions have not got mixed).
merge() can create a result which uses long vectors on 64-bit platforms.
dget() gains a new argument keep.source which defaults to FALSE for speed (dput() and dget() are most often used for data objects where this can make dget() many times faster).
Packages may now use a file of common macro definitions in their help files, and may import definitions from other packages.
A number of macros have been added in the new ‘share/Rd’ directory for use in package overview help pages, and promptPackage() now makes use of them.
tools::parse_Rd() gains a new permissive argument which converts unrecognized macros into text. This is used by utils:::format.bibentry to allow LaTeX markup to be ignored.
options(OutDec =) can now specify a multi-byte character, e.g., options(OutDec = "u00b7") in a UTF-8 locale.
is.recursive(x) is no longer true when x is an external pointer, a weak reference or byte code; the first enables all.equal(x, x) when x .
ls() (aka objects()) and as.list.environment() gain a new argument sorted.
The "source" attribute (which has not been added to functions by R since before R version 2.14.0) is no longer treated as special.
Function returnValue() has been added to give on.exit() code access to a function’s return value for debugging purposes.
crossprod(x, y) allows more matrix coercions when x or y are vectors, now equalling t(x) %*% y in these cases (also reported by Radford Neal). Similarly, tcrossprod(x,y) and %*% work in more cases with vector arguments.
Utility function dynGet() useful for detecting cycles, aka infinite recursions.
The byte-code compiler and interpreter include new instructions that allow many scalar subsetting and assignment and scalar arithmetic operations to be handled more efficiently. This can result in significant performance improvements in scalar numerical code.
apply(m, 2, identity) is now the same as the matrix m when it has named row names.
A new function debuggingState() has been added, allowing to temporarily turn off debugging.
example() gets a new optional argument run.donttest and tools::Rd2ex() a corresponding commentDonttest, with a default such that example(..) in help examples will run donttest code only if used interactively (a change in behaviour).
rbind.data.frame() gains an optional argument make.row.names, for potential speedup.
New function extSoftVersion() to report on the versions of third-party software in use in this session. Currently reports versions of zlib, bzlib, the liblzma from xz, PCRE, ICU, TRE and the iconv implementation.
A similar function grSoftVersion() in package grDevices reports on third-party graphics software.

Function tcltk::tclVersion() reports the Tcl/Tk version.
Calling callGeneric() without arguments now works with primitive generics to some extent.
vapply(x, FUN, FUN.VALUE) is more efficient notably for large length(FUN.VALUE); as extension of PR#16061.
as.table() now allows tables with one or more dimensions of length 0 (such as as.table(integer())).
names(x) now clears the names of call and ... objects.
library() will report a warning when an insufficient dependency version is masking a sufficient one later on the library search path.
A new plot() method for class "raster" has been added.
New check_packages_in_dir_changes() function in package tools for conveniently analyzing how changing sources impacts the check results of their reverse dependencies.
Speed-up from Peter Haverty for ls() and methods:::.requirePackage() speeding up package loading. (PR#16133)
New get0() function, combining exists() and get() in one call, for efficiency.
match.call() gains an envir argument for specifying the environment from which to retrieve the ... in the call, if any; this environment was wrong (or at least undesirable) when thedefinition argument was a function.
topenv() has been made .Internal() for speedup, based on Peter Haverty’s proposal in PR#16140.
getOption() no longer calls options() in the main case.
Optional use of libcurl (version 7.28.0 from Oct 2012 or later) for Internet access:
- capabilities("libcurl") reports if this is available.
- libcurlVersion() reports the version in use, and other details of the "libcurl" build including which URL schemes it supports.
- curlGetHeaders() retrieves the headers for http://, https://, ftp:// and ftps:// URLs: analysis of these headers can provide insights into the ‘existence’ of a URL (it might for example be permanently redirected) and is so used in R CMD check --as-cran.
- download.file() has a new optional method "libcurl" which will handle more URL schemes, follow redirections, and allows simultaneous downloads of multiple URLs.
- url() has a new method "libcurl" which handles more URL schemes and follows redirections. The default method is controlled by a new option url.method, which applies also to the opening of URLs via file() (which happens implicitly in functions such as read.table.)
- When file() or url() is invoked with a https:// or ftps:// URL which the current method cannot handle, it switches to a suitable method if one is available.
(Windows.) The DLLs ‘internet.dll’ and ‘internet2.dll’ have been merged. In this version it is safe to switch (repeatedly) between the internal and Windows internet functions within an Rsession.
The Windows internet functions are still selected by flag –internet2 or setInternet2(). This can be overridden for an url() connection via its new method argument.

download.file() has new method "wininet", selected as the default by –internet2 or setInternet2().
parent.env<- can no longer modify the parent of a locked namespace or namespace imports environment. Contributed by Karl Millar.
New function isLoadedNamespace() for readability and speed.
names(env) now returns all the object names of an environment env, equivalently to ls(env, all.names = TRUE, sorted = FALSE) and also to the names of the corresponding list,names(as.list(env, all.names = TRUE)). Note that although names() returns a character vector, the names have no particular ordering.
The memory manager now grows the heap more aggressively. This reduces the number of garbage collections, in particular while data or code are loaded, at the expense of slightly increasing the memory footprint.
New function trimws() for removing leading/trailing whitespace.
cbind() and rbind() now consider S4 inheritance during S3 dispatch and also obey deparse.level.
cbind() and rbind() will delegate recursively to methods::cbind2 (methods::rbind2) when at least one argument is an S4 object and S3 dispatch fails (due to ambiguity).
(Windows.) download.file(quiet = FALSE) now uses text rather than Windows progress bars in non-interactive use.
New function hsearch_db() in package utils for building and retrieving the help search database used by help.search(), along with functions for inspecting the concepts and keywords in the help search database.
New function .getNamespaceInfo(), a no-check version of getNamespaceInfo() mostly for internal speedups.
The help search system now takes keyword entries in Rd files which are not standard keywords (as given in ‘KEYWORDS’ in the R documentation directory) as concepts. For standard keyword entries the corresponding descriptions are additionally taken as concepts.
New lengths() function for getting the lengths of all elements in a list.
New function toTitleCase() in package tools, tailored to package titles.
The matrix methods of cbind() and rbind() allow matrices as inputs which have 2^31 or more elements. (For cbind(), wish of PR#16198.)
The default method of image() has an explicit check for a numeric or logical matrix (which was always required).
URLencode() will not by default encode further URLs which appear to be already encoded.
BIC(mod) and BIC(mod, mod2) now give non-NA numbers for arima() fitted models, as nobs(mod) now gives the number of “used” observations for such models. This fixes PR#16198, quite differently than proposed there.
The print() methods for "htest", "pairwise.htest" and "power.htest" objects now have a digits argument defaulting to (a function of) getOption("digits"), and influencing all printed numbers coherently. Unavoidably, this changes the display of such test results in some cases.
Code completion for namespaces now recognizes all loaded namespaces, rather than only the ones that are also attached.
The code completion mechanism can now be replaced by a user-specified completer function, for (temporary) situations where the usual code completion is inappropriate.
unzip() will now warn if it is able to detect truncation when unpacking a file of 4GB or more (related to PR#16243).
methods() reports S4 in addition to S3 methods; output is simplified when the class argument is used. .S3methods() and methods::.S4methods() report S3 and S4 methods separately.
Higher order functions such as the apply functions and Reduce() now force arguments to the functions they apply in order to eliminate undesirable interactions between lazy evaluation and variable capture in closures. This resolves PR#16093.

More at http://cran.rstudio.com/

Reference: http://www.r-bloggers.com/r-3-2-0-is-released-using-the-installr-package-to-upgrade-in-windows-os/

Genetic code - Amino Acid

Poonam Mahapatra — Sun, 26 Oct 2014 07:45:58 -0500

The genetic code consists of 64 triplets of nucleotides. These triplets are called codons.With three exceptions, each codon encodes for one of the 20 amino acids used in the synthesis of proteins. That produces some redundancy in the code: most of the amino acids being encoded by more than one codon.

The image summarise all in one.

More at http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/C/Codons.html

Code Golf

Abhi — Wed, 06 Oct 2021 04:17:29 -0500

Code Golf is a game designed to let you show off your code-fu by solving problems in the least number of characters.

Since this is your first time here, I suggest starting with something simple like Fizz Buzz.

Address of the bookmark: https://code.golf/

Five unique traits of effective computational biologist

Jitendra Narayan — Thu, 11 Jul 2013 13:12:51 -0500

Bioinformatics research is driven by large set of software, scripts, and tools to analyse gigantic biological data. Being a great biological programmer or bioinformatician involves more than writing code that works. The biological programmers who rise to the top ranks of their profession are not only good programmer but also expert in biological stuff. Moreover, In order to be a good and effective biological programmer, you need to possess a combination of traits that allow your computational as well as biological skill, experience, and knowledge to produce working code. There are some technically skilled biological programmers who will never be effective because they lack the other important traits needed. Here are top five traits that are necessary to become a great biological programmer.

1. Learn and get updated

Some of the bad biological programmers only learn new technical or non-technical things when it’s absolutely necessary. The good biological programmers learn new technical skills proactively. But great biological programmers not only learn new technical skills on their own but also learn non-technical skills, and have an open mind to sources of knowledge that others may shut out.

In other concrete term, the bad biological programmer learn Perl's regular expression when they started a project on comparative genomics; the good biological programmer learned it a year before because it looked interesting; and the great biological programmer also read about the BioPerl packages, genomics, DNA string, genomic theories, or some similar course of study so that they could understand the results and explain it biologically.

2. Not a merely coder!!!

I often encountered with biological programmer who call themself a hard-core computer programmer and avoid biology. I can almost guarantee that if you are one of them then you are not doing research but merely writing "dry" codes.

According to my supervisor most of the computational biologist, don't know what they are doing biologically. Even they struggle to explain their own programs output and results. Therefore, It is highly advisable to learn basic of biology which can assist you to explain the result and understand your discovery. Always remember you are a researcher not a coder.

3. Be Social with biologist

The computational biologist spends most of the time in from of computers, writing codes. They always think their job is to produce working codes, not technical research perfections. But, they are completely wrong. You should not forget that apart from your computational skills you also need some biologist, other than your supervisor, to explain and make you understand the complex biological mechanism.

I highly recommend your to interact with biotech researchers and learn how do they explain their one graph (which they generally produce after one year of work) biologically. Remember, the origin of your research project is complex biological phenomenon, which is more complex than that of your limited programming rules.

4. Do not search, research for answers

Researching for answers means more than typing several keywords into a search engine or posting a question at Stack Overflow or the BioStars forums. I have entered problems into search engines that generate no results, and every question I posted on Stack Overflow or the BioStars forums never got anything resembling an answer, yet I solved the issues and moved on. I’m not a magician — I just know how to find answers or discover root causes.

Many problems are situational, and if you depend on search engines and forums, you can waste a lot of time going down a rabbit hole and possibly never getting a solution. Learn to perform root cause analysis, learn enough about the underlying system to look for other clues and solutions, and learn to take a long distance view of an issue before deep diving into it.

5. Love and defend your research

You cannot rise to the top in this research profession without loving your work. There are some very good “it’s just a job” biological programmers (I’ve been one at times), but if that is your outlook, you won’t be willing to do whatever it takes to succeed. This idea gets a lot of folks in a huff, because they feel it is a personal insult. “I’m a good programmer, but I have other priorities and can’t make work my life.” I understand completely; I have other priorities too. As much as I hate to say it, when I am passionate about my work, I am willing (though not eager) to abandon my other priorities to finish the job. It is not an insult to say that if you aren’t willing to pull out all the stops you can’t be the best, it is a fact.

You must be passionate about more than programming — you must also be excited about your research, the tools and technology you are using, and so on. I have seen very good and even great biological programmers operating at mediocre levels because something was not a good fit, such as they hated the project or were using a technology they disliked. Therefore, like your research project and get excited about your discoveries. You have not only to discover but also defend your finding with scientific words.

Thanks to all of you for reading.

Bioinformatics Algorithms

Jitendra Narayan — Tue, 16 Jul 2013 03:35:15 -0500

An algorithm is a computable set of steps to achieve a desired result.

We use algorithms every day. For example, a recipe for baking a cake is an algorithm. Most programs, with the exception of some artificial intelligence applications, consist of algorithms. Inventing elegant algorithms -- algorithms that are simple and require the fewest steps possible -- is one of the principal challenges in programming. An algorithm is a description of a procedure which terminates with a result. In other words an algorithm is a set of instructions, sometimes called a procedure or a function, that is used to perform a certain task. This can be a simple process, such as adding two numbers together, or a complex function, such as adding effects to an image. For example, in order to sharpen a digital photo, the algorithm would need to process each pixel in the image and determine which ones to change and how much to change them in order to make the image look sharper.

In mathematics, computer science, and related subjects, an algorithm is an effective method for solving a problem using a finite sequence of instructions. Algorithms are used for calculation, data processing, and many other fields.
Each algorithm is a list of well-defined instructions for completing a task. Starting from an initial state, the instructions describe a computation that proceeds through a well-defined series of successive states, eventually terminating in a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate randomness.

History

The origin of the term comes from the ancients. The concept becomes more precise with the use of variables in mathematics. Algorithm in the sense of what is now used by computers appeared as soon as first mechanical engines were invented.
The word algorithm comes from the name of the 9th century Persian Muslim mathematician Abu Abdullah Muhammad ibn Musa Al-Khwarizmi. The word algorism originally referred only to the rules of performing arithmetic using Hindu-Arabic numerals but evolved via European Latin translation of Al-Khwarizmi's name into algorithm by the 18th century. The use of the word evolved to include all definite procedures for solving problems or performing tasks.
The algorithm of Archimedes gives an approximation of the Pi number.
Eratosthenes has defined an algorithim for retrieving prime numbers.
Averroès (1126-1198) was using algorithmic methods for calculations.
Adelard de Bath (12 th) introduces the algorismus term, from Al-Khwarizmi.
During the 1800's up to the mid-1900's:

- George Boole (1847) has invented the binary algebra, the basis of computers. Actually he has unified logic and calculation in a common symbolism.

- Gottlob Frege (1879) formula language's, that is a lingua characterica, a language written with special symbols, "for pure thought", that is free from rhetorical embellishments... constructed from specific symbols that are manipulated according to definite rules.

- Giuseppe Peano (1888) It's The principles of arithmetic, presented by a new method was the first attempt at an axiomatization of mathematics in a symbolic language.

- Alfred North Whitehead and Bertrand Russell in their Principia Mathematica (1910-1913) has further simplified and amplified the work of Frege.

- Kurt Goëdel (1931) cites the paradox of the liar that completely reduces rules of recursion to numbers.

The concept of algorithm was formalized in 1936 through Alan Turing's Turing machines and Alonzo Church's lambda calculus, which in turn formed the foundation of computer science.
Stephen C. Kleene (1943) defined his now-famous thesis known as the "Church-Turing Thesis". In this context:

" Algorithmic theories... In setting up a complete algorithmic theory, what we do is to describe a procedure, performable for each set of values of the independent variables, which procedure necessarily terminates and in such manner that from the outcome we can read a definite answer, "yes" or "no," to the question, "is the predicate value true?"

Classification

Classification by purpose

Each algorithm has a goal, for example, the purpose of the Quick Sort algorithm is to sort data in ascending or descending order. But the number of goals is infinite, and we have to group them by kind of purposes:

Classification by implementation

An algorithm may be implemeted according to different basical principles.

Recursive or iterative

A recursive algorithm is one that calls itself repeatedly until a certain condition matches. It is a method common to functional programming.
Iterative algorithms use repetitive constructs like loops.
Some problems are better suited for one implementation or the other. For example, the towers of hanoi problem is well understood in recursive implementation. Every recursive version has an iterative equivalent iterative, and vice versa.

Logical or procedural

An algorithm may be viewed as controlled logical deduction.
A logic component expresses the axioms which may be used in the computation and a control component determines the way in which deduction is applied to the axioms.
This is the basis of the logic programming. In pure logic programming languages the control component is fixed and algorithms are specified by supplying only the logic component.

Serial or parallel

Algorithms are usually discussed with the assumption that computers execute one instruction of an algorithm at a time. This is a serial algorithm, as opposed to parallel algorithms, which take advantage of computer architectures to process several instructions at once. They divide the problem into sub-problems and pass them to several processors. Iterative algorithms are generally parallelizable. Sorting algorithms can be parallelized efficiently.

Deterministic or non-deterministic

Deterministic algorithms solve the problem with a predefined process whereas non-deterministic algorithm must perform guesses of best solution at each step through the use of heuristics.

Classification by design paradigm

A design paradigm is a domain in research or class of problems that requires a dedicated kind of algorithm:

Divide and conquer

A divide and conquer algorithm repeatedly reduces an instance of a problem to one or more smaller instances of the same problem (usually recursively), until the instances are small enough to solve easily. One such example of divide and conquer is merge sorting. Sorting can be done on each segment of data after dividing data into segments and sorting of entire data can be obtained in conquer phase by merging them.
The binary search algorithm is an example of a variant of divide and conquer called decrease and conquer algorithm, that solves an identical subproblem and uses the solution of this subproblem to solve the bigger problem.

Dynamic programming

The shortest path in a weighted graph can be found by using the shortest path to the goal from all adjacent vertices.
When the optimal solution to a problem can be constructed from optimal solutions to subproblems, using dynamic programming avoids recomputing solutions that have already been computed.
- The main difference with the "divide and conquer" approach is, subproblems are independent in divide and conquer, where as the overlap of subproblems occur in dynamic programming.
- Dynamic programming and memoization go together. The difference with straightforward recursion is in caching or memoization of recursive calls. Where subproblems are independent, this is useless. By using memoization or maintaining a table of subproblems already solved, dynamic programming reduces the exponential nature of many problems to polynomial complexity.

The greedy method

A greedy algorithm is similar to a dynamic programming algorithm, but the difference is that solutions to the subproblems do not have to be known at each stage. Instead a "greedy" choice can be made of what looks the best solution for the moment.
The most popular greedy algorithm is finding the minimal spanning tree as given by Kruskal.

Linear programming

The problem is expressed as a set of linear inequalities and then an attempt is made to maximize or minimize the inputs. This can solve many problems such as the maximum flow for directed graphs, notably by using the simplex algorithm.
A complex variant of linear programming is called integer programming, where the solution space is restricted to all integers.

Reduction also called transform and conquer

Solve a problem by transforming it into another problem. A simple example: finding the median in an unsorted list is first translating this problem into sorting problem and finding the middle element in sorted list. The main goal of reduction is finding the simplest transformation possible.

Using graphs

Many problems, such as playing chess, can be modeled as problems on graphs. A graph exploration algorithms are used.
This category also includes the search algorithms and backtracking.

The probabilistic and heuristic paradigm

Probabilistic

Those that make some choices randomly.

Genetic

Attempt to find solutions to problems by mimicking biological evolutionary processes, with a cycle of random mutations yielding successive generations of "solutions". Thus, they emulate reproduction and "survival of the fittest".

Heuristic

Whose general purpose is not to find an optimal solution, but an approximate solution where the time or resources to find a perfect solution are not practical.

Classification by complexity

Some algorithms complete in linear time, and some complete in exponential amount of time, and some never complete.

Algorithms resources on net.

Graph Algorithms in Bioinformatics

Bioinformatics Algorithms Description

Bioinformatics Algorithms Course Page

Bioinformatics Algorithm Demonstrations

Introduction to Bioinformatics Algorithms Lectures 1-2 by Dr. Max Alekseyev USC, 2009

Online Lectures on Bioinformatics

Sequence Alignment Algorithms

Algorithm for sequence alignment: dynamic programming

Network Protocol Analysis using Bioinformatics Algorithms