SHARPCLAW: Synteny, Homology And Repeat Pre-curation for Chromosome-Level Assembly Workflows

I’ve been a recovering acronymiser for some time, with the last few bioinformatics given more punny names, like Diploidocus, Taxolotl, Telociraptor and SynBad. But I was pushed for time preparing a conference poster and needed a name for a new workflow, and SHARPCLAW - Synteny, Homology And Repeat Pre-curation for Chromosome-Level Assembly Workflows - was born. I’ve not yet crow-barred in additional meaning, so for now this has definite ad hoc status.

IRENE - Image, Reconstruct, Erase Noise, Etc.

This month’s Nature Audio file features a device, IRENE, designed to:

acquire digital maps of the surface of the media, without contact, and then apply image analysis methods to recover the audio data and reduce noise.

IRENE stands for Image, Reconstruct, Erase Noise, Etc. and was named after one of the first reconstructed audio recordings: “Goodnight Irene”, written by H. Ledbetter and J. Lomax, performed by the Weavers (1950). This earns IRENE the much prized pre-hoc classification.

Here more about IRENE here.

FUBAR - Fast Unconstrained Bayesian AppRoximation

From the authors that brought you BUSTED (Branch-site Unrestricted Statistical Test for Episodic Diversification), behold FUBAR: Fast Unconstrained Bayesian AppRoximation. (Doubly from the authors in this case, as the first author gave the tip-off.)

Despite it’s intranym status, FUBAR gets an extra geek hat-tip for being a homonym of “foo bar”. (Although in looking that up, I came across the original FUBAR acronym, which is hopefully not reflective of their method!)

Abstract

Model-based analyses of natural selection often categorize sites into a relatively small number of site classes. Forcing each site to belong to one of these classes places unrealistic constraints on the distribution of selection parameters, which can result in misleading inference due to model misspecification. We present an approximate hierarchical Bayesian method using a Markov chain Monte Carlo (MCMC) routine that ensures robustness against model misspecification by averaging over a large number of predefined site classes. This leaves the distribution of selection parameters essentially unconstrained, and also allows sites experiencing positive and purifying selection to be identified orders of magnitude faster than by existing methods. We demonstrate that popular random effects likelihood methods can produce misleading results when sites assigned to the same site class experience different levels of positive or purifying selection–an unavoidable scenario when using a small number of site classes. Our Fast Unconstrained Bayesian AppRoximation (FUBAR) is unaffected by this problem, while achieving higher power than existing unconstrained (fixed effects likelihood) methods. The speed advantage of FUBAR allows us to analyze larger data sets than other methods: We illustrate this on a large influenza hemagglutinin data set (3,142 sequences). FUBAR is available as a batch file within the latest HyPhy distribution (http://www.hyphy.org), as well as on the Datamonkey web server (http://www.datamonkey.org/).

• Murrell B, Moola S, Mabona A, Weighill T, Sheward D, Kosakovsky Pond SL & Scheffler K (2013). FUBAR: a fast, unconstrained bayesian approximation for inferring selection. Mol Biol Evol. 30(5):1196-205. PMID: 23420840

BUSTED - Branch-site Unrestricted Statistical Test for Episodic Diversification

This paper popped up in my PubCrawler feed today:

Murrell B et al. (2015). Gene-wide identification of episodic selection. Mol Biol Evol. 2015 Feb 19. pii: msv035.

We present BUSTED, a new approach to identifying gene-wide evidence of episodic positive selection, where the non-synonymous substitution rate is transiently greater than the synonymous rate. BUSTED can be used either on an entire phylogeny (without requiring an a priori hypothesis regarding which branches are under positive selection) or on a pre-specified subset of foreground lineages (if a suitable a priori hypothesis is available). Selection is modeled as varying stochastically over branches and sites, and we propose a computationally inexpensive evidence metric for identifying sites under episodic positive selection on any foreground branches. We compare BUSTED to existing models on simulated and empirical data. An implementation is available on www.datamonkey.org/busted, with a widget allowing the interactive specification of foreground branches.

From the Introduction, we find that BUSTED is indeed an orca-worthy contrived acronym: BUSTED - Branch-site Unrestricted Statistical Test for Episodic Diversification.

SANTA - Spatial Analysis of NeTwork Associations

A festive bioinformatics acronym today: SANTA - Spatial Analysis of NeTwork Associations. The authors don't make a big deal of the acronym in the paper but it seemed contrived enough for a Christmas ORCA entry.

Abstract

Linking networks of molecular interactions to cellular functions and phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach (called SANTA) quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes. As a general association measure, SANTA can (i) functionally annotate experimentally derived networks using a collection of curated gene sets and (ii) annotate experimentally derived gene sets using a collection of curated networks, as well as (iii) prioritize genes for follow-up analyses. We exemplify the efficacy of SANTA in several case studies using the S. cerevisiae genetic interaction network and genome-wide RNAi screens in cancer cell lines. Our theory, simulations, and applications show that SANTA provides a principled statistical way to quantify the association between molecular networks and cellular functions and phenotypes. SANTA is available from http://bioconductor.org/packages/release​/bioc/html/SANTA.html.

Ref: Cornish AJ & Markowetz F (2014) SANTA: Quantifying the Functional Content of Molecular Networks. PLoS Comput Biol 10(9): e1003808.

REACH - Registration, Evaluation, Authorisation and Restriction of Chemicals

REACH - Registration, Evaluation, Authorisation and Restriction of Chemicals is a fine example of acronym contrivance (although lacking the panache of a good pre hoc concoction). Not got the right words for something catchy? Just ignore the inconvenient word!

“[REACH] streamlines and improves the former legislative framework on chemicals of the European Union (EU). The main aims of REACH are to ensure a high level of protection of human health and the environment from the risks that can be posed by chemicals, the promotion of alternative test methods, the free circulation of substances on the internal market and enhancing competitiveness and innovation.”

I guess you can’t blame them for trying to make it more interesting.

MUSIC - MUltiScale enrIchment Calling

Over on the ACGT blog, Keith Bradnam has another JABBA Award (and a nice new JABBA logo):

MUSIC - Multiscale Enrichment Calling.

I'm not sure what the connection between MUSIC and ChIP-Seq is but the authors seemed pretty determined. His post is actually a twofer, as it also draws attention to an equally contrived an unfathomable intranym:

MuSiC - Multiple Sequence Alignment with Constraints

Read more at Keith’s blog!

DREAM - Dialogue for Reverse Engineering Assessments and Methods

According to the website, DREAM is a Dialogue for Reverse Engineering Assessments and Methods:

“The main objective is to catalyze the interaction between experiment and theory in the area of cellular network inference and quantitative model building in systems biology.”

Not clear?

“DREAM (Dialogue for Reverse Engineering Assessments and Methods) poses fundamental questions about systems biology, and invites participants to propose solutions. The main objective is to catalyze the interaction between theory and experiment, specifically in the area of cellular network inference and quantitative model building. DREAM challenges address how we can assess the quality of our descriptions of networks that underlie biological systems, and of our predictions of the outcomes of novel experiments. These are not simple questions. Researchers have used a variety of algorithms to deduce the structure of biological networks and/or to predict the outcome of perturbations to their systems. They have also evaluated the success of their methodologies using a diverse set of non-standardised metrics. What is still needed, and what DREAM aims to achieve, is a fair comparison of the strengths and weaknesses of these methods and a clear sense of the reliability of the models that researchers create.”

I know a bit about Systems Biology but I must admit to being confused about the “Assessment and Methods” part of DREAM. I think it means that DREAM is about assessing methods and models for reverse engineering biological systems (i.e. Systems Biology), even though it reads that they are trying to reverse engineer assessments and methods. Such dedication to the acronym over clarity makes DREAM a worthy ORCA entry.

ILIaD - Institute for Learning Innovation and Development

The learning and teaching unit at the University of Southampton has had a few different names over the years but its latest incarnation is ORCA-worthy with some selective use of prepositions: ILIaD - the Institute for Learning Innovation and Development. Find out more at the ILIaD website. (Although ILIaD is undoubtedly replete with heroes, I think this is ad hoc.)

MAGIC - the Meta-Analyses of Glucose and Insulin-related traits Consortium

The final human genetics consortium (for now) is MAGIC - the Meta-Analyses of Glucose and Insulin-related traits Consortium, another co-author of the DIAGRAM consortium. This is probably the most contrived of the lot, in that it failed to use all its words but did include “Consortium”. At the same time, being part of MAGIC has a certain appeal above CHARGE, DIAGRAM or GIANT.

“MAGIC (the Meta-Analyses of Glucose and Insulin-related traits Consortium) represents a collaborative effort to combine data from multiple GWAS to identify additional loci that impact on glycemic and metabolic traits.

MAGIC investigators have initially studied fasting glucose, fasting insulin, 2h glucose and HBA1c, as well as performed meta-analysis of more sophisticated measures of insulin secretion and sensitivity. Through these efforts, dozens of loci influencing these traits have been idenified, a subset of which also influence risk of type 2 diabetes.”

There is a certain degree of magic involved in a good genome-wide association study (GWAS) but I still rate this one ad hoc.

If contrived consortium acronyms are your thing, you can probably do a lot worse than sign up to the Table of Contents alerts for the journal Nature Genetics.

GIANT - Genetic Investigation of ANthropometric Traits

Human genetics consortium number three is the GIANT - Genetic Investigation of ANthropometric Traits - consortium, a co-author of one of the recent DIAGRAM papers in Nature Genetics.

“The Genetic Investigation of ANthropometric Traits (GIANT) consortium is an international collaboration that seeks to identify genetic loci that modulate human body size and shape, including height and measures of obesity. The GIANT consortium is a collaboration between investigators from many different groups, institutions, countries, and studies, and the results represent their combined efforts. The primary approach has been meta-analysis of genome-wide association data and other large-scale genetic data sets. Anthropometric traits that have been studied by GIANT include body mass index (BMI), height, and traits related to waist circumference (such as waist-hip ratio adjusted for BMI, or WHRadjBMI). Thus far, the GIANT consortium has identified common genetic variants at hundreds of loci that are associated with anthropometric traits.”

Genome-wide association studies are big by nature, and GIANT does have a lot of participating cohorts and groups on their webpage, so I think that GIANT can be given a post hoc rating.

DIAGRAM - DIAbetes Genetics Replication And Meta-analysis

The DIAGRAM - DIAbetes Genetics Replication And Meta-analysis - consortium is the second ORCA entry this month for human genetics consortia, which seem to be almost as productive a source of contrived acronyms as bioinformatics.

“The DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium is a grouping of researchers with shared interests in performing large-scale studies to characterise the genetic basis of type 2 diabetes, and a principal focus on samples of European descent.”

You can read more on their website, which features an array of additional acronyms (including some future ORCA entries).

CHARGE - Cohorts for Heart and Aging Research in Genomic Epidemiology

The CHARGE - Cohorts for Heart and Aging Research in Genomic Epidemiology - consortium is the first of four human genetics consortia to hit ORCA this month.

I’m not really sure what tigers have to do with hearts or aging, or even charging for that matter, but it’s a nice logo. Coordinating research with that many participants must be challenging enough - I am not sure how you contrive an acronym and logo that everyone agrees with!

According to the CHARGE consortium website:

"The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium was formed to facilitate genome-wide association study meta-analyses and replication opportunities among multiple large and well-phenotyped longitudinal cohort studies."

• Psaty BM, O’Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, Rotter JI, Uitterlinden AG, Harris TB, Witteman JCM, Boerwinkle E, on behalf of the CHARGE Consortium (2009) Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from five cohorts. Circ Cardiovasc Genet. 2:73-80.

PEPPER - Protein complex Expansion using Protein-Protein intERactions

Another offering from the ever-fruitful world of bioinformatics today - with a fruit, fittingly enough.

PEPPER - Protein complex Expansion using Protein-Protein intERactions - pulls out all the stops when it comes to contriving a functional word, skipping words and using internal letters.

For the curious, PEPPER is available from the Cytoscape App Store and is:

“designed to identify protein complexes as densely connected subnetworks from seed lists of proteins derived from proteomic studies. Pepper identifies connected subgraph by using multi-objective optimization involving two functions: (i) the coverage, a solution must contain as many proteins from the seed as possible, (ii) the density, the proteins of a solution must be as connected as possible, using only interactions from a proteome-wide interaction network.”

Despite its use of “seed lists”, I don’t think there is a direct connection with peppers, so PEPPER is being classified as an ad hoc intranym.

I’m not sure if there’s a bigger version of the logo available but if you look carefully, you can see the little chilli peppers.

Winterhalter C et al. (2014) PEPPER: cytoscape app for protein complex expansion using protein-protein interaction networks. Bioinformatics. Aug 18. pii: btu517. [Epub ahead of print]

WImpiBLAST - Web Interface for mpiBLAST

Although BLAST is a deserved ORCAcronym in its own right, it is also notable for the number of spin-offs and add-ons that it has produced. One such program is mpiBLAST, an open-source parallelisation of BLAST. This is turn has spawned WImpiBLAST - Web Interface for mpiBLAST. I’m not sure if WImpiBLAST deserves a new “anti hoc” class of acronym, as parallelisation of BLAST on a supercomputer is anything but wimpy. Ad hoc will do for now.

You can find out more at RNA-Seq Blog or the paper:

• Sharma P & Mantri SS (2014) “WImpiBLAST: Web Interface for mpiBLAST to Help Biologists Perform Large-Scale Annotation Using High Performance Computing.” PLoS ONE 9(6): e101144.

BLAST - Basic Local Alignment Search Tool

If there is one bioinformatics program that every biologist has heard of, it is BLAST: Basic Local Alignment Search Tool. In fact, BLAST is so popular and famous that many people probably don’t know that it is an acronym nor what it stands for; “to BLAST” has become a verb in common use. For non-biologists, BLAST is a homology search tool, which means that it finds similarities between biological (nucleotide or protein) sequences, and biologists will frequently “BLAST a sequence against a database” to find similar sequences.

The original BLAST algorithm is pretty old, which is part of the reason for its widespread fame. I’m not sure of the stats but the original Altschul et al. (1990) paper must be one of the most cited of all time.

• Altschul S, Gish W, Miller W, Myers E & Lipman D (1990). “Basic local alignment search tool”. Journal of Molecular Biology 215 (3): 403–410.

Certainly a worth addition to ORCA. (Although the main motivation for adding it is for the next post!) Given that BLAST works by fragmenting the query sequence into pieces for the initial search, I think that it rates at least as a post hoc.

MFSPSSMpred - Masked, Filtered and Smoothed Position-Specific Scoring Matrix-based predictor

Today’s post is a bit odd, as it is an anti-ORCA acronym. There is a reason why contrived acronyms exist, and I think that MFSPSSMpred is a prime example. The acronym itself is almost informative, although it fails to identify what it is trying to predict. (Short molecular recognition features in this case.) Still, imagine yourself in a lab meeting, trying to tell your supervisor which prediction tool you were using from memory!

MFSPSSMpred does have one thing going for it, though, and that’s Google-friendliness. Providing you spell it right, that is!

P.S. Whilst Googling MFSPSSMpred, I discovered that OCTAGON-winning Keith Bradnam got there first and MFSPSSMpred is a JABBA Award winner!

ARENA - Australian Renewable ENergy Agency

ARENA is the Australian Renewable Energy Agency,

an independent agency established by the Australian Government on 1 July 2012. We have two objectives: to improve the competitiveness of renewable energy technologies, and to increase the supply of renewable energy in Australia.

Unfortunately, ARENA was one of the losers from the recent Australian budget and might not be around much longer.

HOTAIR - HOX Transcript Antisense RNA

According to Wikipedia:

HOTAIR (for HOX antisense intergenic RNA) is a human gene located on chromosome 12. It is the first example of an RNA expressed on one chromosome that has been found to influence transcription on another chromosome.

However, Wikipedia itself cites Genecards, which has a slightly different metanym expansion (without any missing letters) for HOTAIR: HOX Transcript Antisense RNA. Either way, a very interesting gene and, though ad hoc intranym, certainly contrived enough for ORCA!

(HOTAIR possibly needs a new ORCA classification because both “metanym” sub-acronyms - HOX (Homeobox) and RNA (Ribonucleic acid) are themselves “itranyms”, as is HOTAIR.)

MODULUS - Methods Of Determining and Understanding Light elements from Unequivocal Stable isotope compositions

Another one from Rosetta, MODULUS - Methods Of Determining and Understanding Light elements from Unequivocal Stable isotope compositions - is described in Cosmos as part of the PTOLEMY instrumentation "to understand the geochemistry of light elements, such as hydrogen, carbon, nitrogen and oxygen".

That’s a lot of words and definitely an ORCA level of commitment to contrive a word out of it, even if they were not able to use them all!