Keywords and Expertise

Compare two or more entities, typically the sequence or structure (or derivatives) of macromolecules, to identify equivalent subunits.

Group together some data entities on the basis of similarities such that entities in the same group (cluster) are more similar to each other than to those in other groups (clusters).

Apply analytical methods to existing data of a specific type.|This excludes non-analytical methods that read and write the same basic type of data (for that, see 'Data handling').

Map properties to positions on an biological entity (typically a molecular sequence or structure), or assemble such an entity from constituent parts.

Counting and measuring experimentally determined observations into quantities.

Identify base (nucleobase) sequence from a fluorescence 'trace' data generated by an automated DNA sequencer.

Mathematical determination of the value of something, typically a properly of a molecule.

Annotate an entity (typically a biological or biomedical database entity) with terms from a controlled vocabulary.|This is a broad concept and is used a placeholder for other, more specific concepts.

Filter a set of files or data items according to some property.

Link together a non-contiguous series of genomic sequences into a scaffold, consisting of sequences separated by gaps of known length. The sequences that are linked are typically typically contigs; contiguous sequences corresponding to read overlaps.|Scaffold may be positioned along a chromosome physical map to create a "golden path".

Construction of a single sequence assembly of all reads from different samples, typically as part of a comparative metagenomic analysis.

Detect, identify and map mutations, such as single nucleotide polymorphisms, short indels and structural variants, in multiple DNA sequences. Typically the alignment and comparison of the fluorescent traces produced by DNA sequencing hardware, to study genomic alterations.|Somatic variant calling is the detection of variations established in somatic cells and hence not inherited as a germ line variant.|Variant detection|Methods often utilise a database of aligned reads.

Raw sequence data quality control.|Analyse raw sequence data from a sequencing pipeline and identify (and possiby fix) problems.

The process of assembling many short DNA sequences together such thay they represent the original chromosomes from which the DNA originated.

Identify putative protein-binding regions in a genome sequence from analysis of Chip-sequencing data or ChIP-on-chip data.|Chip-sequencing combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to generate a set of reads, which are aligned to a genome sequence. The enriched areas contain the binding sites of DNA-associated proteins. For example, a transcription factor binding site. ChIP-on-chip in contrast combines chromatin immunoprecipitation ('ChIP') with microarray ('chip'). "Peak-pair calling" is similar to "Peak calling" in the context of ChIP-exo.

Align (identify equivalent sites within) molecular sequences.|See also "Read mapping"

The processing of reads from high-throughput sequencing machines.

Process (read and/or write) expression data from experiments measuring molecules (e.g. omics data), including analysis of one or more expression profiles, typically to interpret them in functional terms.

Build clusters of similar sequences, typically using scores from pair-wise alignment or other comparison of the sequences.|The clusters may be output or used internally for some other purpose.

Analyse one or more known molecular sequences.

Generate, process or analyse a biological pathway.

Annotate a molecular sequence record with terms from a controlled vocabulary.

Analyze data from RNA-seq experiments.

Model protein-ligand (for example protein-peptide) binding using comparative modelling or other techniques.|Virtual screening is used in drug discovery to search libraries of small molecules in order to identify those molecules which are most likely to bind to a drug target (typically a protein receptor or enzyme).|Methods aim to predict the position and orientation of a ligand bound to a protein receptor or enzyme.

Detects chimeric sequences (chimeras) from a sequence alignment.

Annotate a genome sequence with terms from a controlled vocabulary.

Variant filtering is used to eliminate false positive variants based for example on base calling quality, strand and position information, and mapping info.

Infer a transcriptome sequence by analysis of short sequence reads.

Combine (align and merge) overlapping fragments of a DNA sequence to reconstruct the original sequence.|For example, assemble overlapping reads from paired-end sequencers into contigs (a contiguous sequence corresponding to read overlaps). Or assemble contigs, for example ESTs and genomic DNA fragments, depending on the detected fragment overlaps.

Analyse cytosine methylation states in nucleic acid sequences.

Model or simulate some biological entity or system, typically using mathematical techniques including dynamical systems, statistical models, differential equations, and game theoretic models.

Infer haplotypes, either alleles at multiple loci that are transmitted together on the same chromosome, or a set of single nucleotide polymorphisms (SNPs) on a single chromatid that are statistically associated.|Haplotype inference can help in population genetic studies and the identification of complex disease genes, , and is typically based on aligned single nucleotide polymorphism (SNP) fragments. Haplotype comparison is a useful way to characterize the genetic variation between individuals. An individual's haplotype describes which nucleotide base occurs at each position for a set of common SNPs. Tools might use combinatorial functions (for example parsimony) or a likelihood function or model with optimisation such as minimum error correction (MEC) model, expectation-maximisation algorithm (EM), genetic algorithm or Markov chain Monte Carlo (MCMC).

The measurement of the activity (expression) of multiple genes in a cell, tissue, sample etc., in order to get an impression of biological function.|Gene expression profiling generates some sort of gene expression profile, for example from microarray data.

Analyse flexibility and motion in protein structure.|Use this concept for analysis of flexible and rigid residues, local chain deformability, regions undergoing conformational change, molecular vibrations or fluctuational dynamics, domain motions or other large-scale structural transitions in a protein structure.

Predict or detect ligand-binding sites in proteins; a region of a protein which reversibly binds a ligand for some biochemical purpose, such as transport or regulation of protein function.

Analyse the interactions of proteins with other proteins.

Classifiication (typically of molecular sequences) by assignment to some taxonomic hierarchy.

Align more than two molecular sequences.|This includes methods that use an existing alignment, for example to incorporate sequences into an alignment, or combine several multiple alignments into a single, improved alignment.

Detect, predict and identify genes or components of genes in DNA sequences, including promoters, coding regions, splice sites, etc.|Methods for gene prediction might be ab initio, based on phylogenetic comparisons, use motifs, sequence features, support vector machine, alignment etc.

Analyse a genetic variation, for example to annotate its location, alleles, classification, and effects on individual transcripts predicted for a gene model.|Genetic variation annotation provides contextual interpretation of coding SNP consequences in transcripts. It allows comparisons to be made between variation data in different populations or strains for the same transcript.

Analyse an existing phylogenetic tree or trees, typically to detect features or make predictions.|Phylgenetic modelling is the modelling of trait evolution and prediction of trait values using phylogeny as a basis.

Identify insertion, deletion and duplication events from a sequence alignment.|Tools might use a genetic algorithm, quartet-mapping, bootscanning, graphical methods, random forest model and so on.

Pre-process sequence reads to ensure (or improve) quality and reliability.|For example process paired end reads to trim low quality ends remove short sequences, identify sequence inserts, detect chimeric reads, or remove low quality sequnces including vector, adaptor, low complexity and contaminant sequences. Sequences might come from genomic DNA library, EST libraries, SSH library and so on.

Annotate one or more sequences with functional information, such as cellular processes or metaobolic pathways, by reference to a controlled vocabulary - invariably the Gene Ontology (GO).

Perform a statistical data operation of some type, e.g. calibration or validation.

Generate a genetic (linkage) map of a DNA sequence (typically a chromosome) showing the relative positions of genetic markers based on estimation of non-physical distances.|Mapping involves ordering genetic loci along a chromosome and estimating the physical distance between loci. A genetic map shows the relative (not physical) position of known genes and genetic markers.|This includes mapping of the genetic architecture of dynamic complex traits (functional mapping), e.g. by characterisation of the underlying quantitative trait loci (QTLs) or nucleotides (QTNs).

A statistical calculation to estimate the relationships among variables.

Predict or detect active sites in proteins; the region of an enzyme which binds a substrate bind and catalyses a reaction.