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A motif visualization in which residue heights are scaled relative to their statistical significance. It supports real-time conditional probability calculations and visualizations.

Builds on probability theory to visualize significant conserved sequence patterns in multiple peptide sequence alignments against background (reference) sequence sets that can be tailored to the studied system and the used protocol. The unique advantages of iceLogo compared to other sequence logo creating tools is that iceLogo has a more dynamic nature and is more correct and complete in the analysis of conserved sequence patterns.

A web based application designed to make the generation of sequence logos easy and painless.

A web-based tool that generates sequence logos from various input sources.

A web-based sequence logo generator. Sequence logos are a graphical representation of the information content stored in a multiple sequence alignment (MSA) and provide a compact and highly intuitive representation of the position-specific amino acid composition of binding motifs, active sites, etc. in biological sequences.

Two Sample Logo

A Web-based tool that detects and displays statistically significant differences in position-specific symbol compositions between two sets of multiple sequence alignments. The source code is distributed under the MIT Open Source license agreement and is available for download free of charge.

FACIL genetic code prediction tool

Infers the genetic code directly from a newly sequenced, unannotated genome.

A Web based application designed to make the generation of sequence logos from biological sequence alignments simple.

Helps visualize the conservation and entropy of orthologous DNA and protein sequences by presenting them as one long sequence logo that can be zoomed in and out of, from an overview of the entire sequence down to just a few residues at a time. A search function enables users to find motifs such as cis-elements in promoter regions by simply ‘drawing’ a sequence logo representation of the desired motif as a query. Gene Slider combines the ability to make long sequence logos with a search panel that automatically highlights motifs of interest, even when there is a high degree of wobble.

Generates publication-quality sequence logos. ggseqlogo employs a method based on the use of polygons to draw elongated letters in ggplot2. It provides numerous features that allow tweaking of the sequence logo. The tool offers high degree of flexibility and customisability through integration with ggplot2. It allows elements such as rectangles, lines, arrows and text to be precisely overlaid onto the logo, programmatically.

A tool for creating logos representing both sequence alignments and profile hidden Markov models.

Provides an intuitive visualization of motif differences. DiffLogo is an easy-to-use tool for a fast and efficient comparison of motifs. It may be applied by users with only basic knowledge in R and is highly configurable and extensible for advanced users. It enables the illustration and investigation of differences between highly similar motifs such as binding patterns of transcription factors for different cell types, treatments, and algorithmic approaches. Using DiffLogo, it is easily possible to compare motifs from different sources, so DiffLogo facilitates decision making, knowledge sharing, and the presentation of results.

Allows users to produce generalized string-based logo. Logolas provides a method to highlight both enrichment and depletion of elements at each position in a sequence. It offers several features such as: the ability to use strings instead of characters; various customizable styles and color palettes; several methods for scaling stack heights; and ease of integrating logo plots with external graphics like ggplot.

Generates DNA sequence logos. DNAlogo can read matrices designating the bit values of each character directly. It can create both bit logos and frequency logos. Frequency logos contain only the information of frequencies of the four nucleotides, but no information of sequence conservation. DNAlogo minimizes the bias introduced by different GC contents. It creates DNA sequence logos with simple operation, and no programming knowledge needed.

kpLogo / k-mer probability logo

Integrates detection and visualization of position-specific ultra-short motifs within macromolecules. kpLogo overcomes the limitations of conventional motif-visualization tools in handling positional interdependencies and utilizing ranked or weighted sequences increasingly available from high-throughput assays. It generates a new type of logo plot called the k-mer logo, in which at each position the most significant k-mer is plotted vertically with the total height scaled to its P value or test statistics, as appropriate. kpLogo can be accessed at a simple webserver which generates frequency, information-content, and probability logos in addition to k-mer logos. Its source code is freely available.

Sequence logos for DNA sequence alignments. seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo.

Uses amino acid sequence alignment to generate sequences logos. SeqLogos allows visualization of sequence patterns and represents an informative alternative to consensus sequence.

Permits to represent multiple motifs with different similarity scores. motifStack is able to draw amino acid sequence as easy as to draw DNA/RNA sequence. It allows users to select the font type and symbol colors. The tool calculates mean of multiple motifs position to merge them.

Enables to observe the intra-motif dependencies and to visualize the position-specific nucleotide diversity or consensus. CircularLogo allows users to create sequence logo for revealing pairwise dependencies among residues within a motif. It offers the panorama of DNA or RNA motifs taking into consideration the intra-site dependencies. This tool can depict complex dependencies within motifs and reveal biomolecular structure (such as stem structures in tRNA).

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Big data scientist National institute of Plant Gneome Reseaech (NIPGR)

Research Interests Gene fusion detection in Plants Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions are well known in case of human. But, in plants, this phenomenon is not yet explored. We are planning to discover the fusion transcripts/gene fusions in different type of plants by using RNA-Seq datasets. Further, we are planning to understand the mechanism of gene fusion formation and significance of fusions in plants. Whole genome and transcriptome sequencing data analysis of Plants In this era of Next Generation Sequencing (NGS), there is huge amount of sequencing data available in the public domain. Any novel finding from these available datasets is major challenge for a computational biologist. We are interested in the analysis of whole genome and transcriptome sequencing data of different plants to fetch out the useful information from those datasets, with the help of bioinformatics tools. Currently, we are planning to study the gene clusters of secondary metabolite pathways in different plants. Development of webservers, databases and computational pipelines for plant research Development of database is necessary to compile and share the information with scientific community. We are dedicated to develop useful databases and webserver for plant research. Another area of interest is to develop automated pipelines and tools for the analysis of high throughput genomics data, generated by NGS technologies. Professional & Academic Background Staff Scientist II (May 2017- present): National Institute of Plant Genome Research (NIPGR), New Delhi, India Postdoctoral Research Associate (2015-2017): University Of Virginia, Charlottesville, VA, USA Research Scientist (2014-2015): Sir Ganga Ram Hospital, New Delhi, India PhD Bioinformatics (2009-2014): Bioinformatics Centre, Institute of Microbial Technology (IMTECH), Chandigarh under Jawaharlal Nehru University (JNU), New Delhi, India M.Sc. Life Sciences (2007-2009): Jawaharlal Nehru University (JNU), New Delhi, India B.Sc. Biotechnology (2004-2007): Jamia Millia Islamia (JMI), New Delhi, India Awards and Fellowships Junior and Senior Research Fellowship (2009-2014): Council of Scientific and Industrial Research (CSIR), New Delhi, India GATE (Graduate Aptitude Test in Engineering): Qualified in years 2008 and 2009 Scientific Contributions/ Recognitions Associate editor: Journal of Translational Medicine. Editorial Board Member of Journal: Theoretical Biology and Medical Modelling. Reviewer: PloS One, BMC Genomics, BMC Bioinformatics, BMC Biology, BMC Biotechnology, Frontiers in Physiology and several other journals. Web Resources/ Databases (Developed/ Contributed) A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer (http://www.imtech.res.in/raghava/cancertope/) GenomeABC: A webserver for benchmarking of genome assemblers. (http://crdd.osdd.net/raghava/genomeabc/). Genomics web portal page. (http://crdd.osdd.net/raghava/genomesrs/). Map/Alignment module of CancerDr: Cancer Drug Resistance Database. (http://crdd.osdd.net/raghava/cancerdr/). Short reads and contigs alignment module of PCMDB: Pancreatic cancer methylation database. (http://crdd.osdd.net/raghava/pcmdb/). Burkholderia sp. SJ98 database. (http://crdd.osdd.net/raghava/genomesrs/burkholderia/). Rhodococcus imtechensis RKJ300 database. (http://crdd.osdd.net/raghava/genomesrs/rkj300/). Genotrick: A pipeline for whole genome assembly and annotation of Genomes (http://crdd.osdd.net/raghava/genomesrs/genotrick/) Development of Debian packages in OSDDlinux: A Customized Operating System for Drug Discovery. (http://osddlinux.osdd.net/). A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of Mycobacterium tuberculosis. (http://crdd.osdd.net/raghava/mtbveb/).

FusionFinder comrad

Mahantesh Biradar

‎Graduate PhD researcher focusing on Cardiovascular Epidemiology & Social Media Enthusiast Research Stash

I'm a Graduate PhD researcher in the area of Biomedical Science and focussing on the Pharmacogenomics/Pharmacogenomics of Diabetes and related complications

Sequence logo generation software tools | Comparative genomics

Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position.

Source text:
(Workman et al., 2005) enoLOGOS: a versatile web tool for energy normalized sequence logos. Nucleic Acids Res.

Parent category

Comparative genomics

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