Abstract

BackgroundWhile the local-mode HMMER3 is notable for its massive speed improvement, the slower glocal-mode HMMER2 is more exact for domain annotation by enforcing full domain-to-sequence alignments. Since a unit of domain necessarily implies a unit of function, local-mode HMMER3 alone remains insufficient for precise function annotation tasks. In addition, the incomparable E-values for the same domain model by different HMMER builds create difficulty when checking for domain annotation consistency on a large-scale basis.ResultsIn this work, both the speed of HMMER3 and glocal-mode alignment of HMMER2 are combined within the xHMMER3x2 framework for tackling the large-scale domain annotation task. Briefly, HMMER3 is utilized for initial domain detection so that HMMER2 can subsequently perform the glocal-mode, sequence-to-full-domain alignments for the detected HMMER3 hits. An E-value calibration procedure is required to ensure that the search space by HMMER2 is sufficiently replicated by HMMER3. We find that the latter is straightforwardly possible for ~80% of the models in the Pfam domain library (release 29). However in the case of the remaining ~20% of HMMER3 domain models, the respective HMMER2 counterparts are more sensitive. Thus, HMMER3 searches alone are insufficient to ensure sensitivity and a HMMER2-based search needs to be initiated. When tested on the set of UniProt human sequences, xHMMER3x2 can be configured to be between 7× and 201× faster than HMMER2, but with descending domain detection sensitivity from 99.8 to 95.7% with respect to HMMER2 alone; HMMER3’s sensitivity was 95.7%. At extremes, xHMMER3x2 is either the slow glocal-mode HMMER2 or the fast HMMER3 with glocal-mode. Finally, the E-values to false-positive rates (FPR) mapping by xHMMER3x2 allows E-values of different model builds to be compared, so that any annotation discrepancies in a large-scale annotation exercise can be flagged for further examination by dissectHMMER.ConclusionThe xHMMER3x2 workflow allows large-scale domain annotation speed to be drastically improved over HMMER2 without compromising for domain-detection with regard to sensitivity and sequence-to-domain alignment incompleteness. The xHMMER3x2 code and its webserver (for Pfam release 27, 28 and 29) are freely available at http://xhmmer3x2.bii.a-star.edu.sg/.ReviewersReviewed by Thomas Dandekar, L. Aravind, Oliviero Carugo and Shamil Sunyaev. For the full reviews, please go to the Reviewers’ comments section.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0163-0) contains supplementary material, which is available to authorized users.

Highlights

  • While the local-mode HMMER3 is notable for its massive speed improvement, the slower glocalmode HMMER2 is more exact for domain annotation by enforcing full domain-to-sequence alignments

  • Post-calibration finding 1: On average, HMMER3 E-values need to be more stringent than HMMER2 glocal-mode E-values to exhibit the same false-positive rates (FPR) The function annotation task via the HMMER algorithm is innately coupled to the domain libraries

  • This entails the objective comparison of the receiver operating characteristic (ROC) curves generated by the two HMMER builds (i.e., HMMER2 and HMMER3) for each domain model

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Summary

Introduction

While the local-mode HMMER3 is notable for its massive speed improvement, the slower glocalmode HMMER2 is more exact for domain annotation by enforcing full domain-to-sequence alignments. Since a unit of domain necessarily implies a unit of function, local-mode HMMER3 alone remains insufficient for precise function annotation tasks. The incomparable E-values for the same domain model by different HMMER builds create difficulty when checking for domain annotation consistency on a large-scale basis. The value of biomedical and biotechnological applications from biomolecular sequence information is generally limited by the degree of functional annotation of non-coding genomic regions, protein-coding genes and the proteins themselves [1, 2]. Functional annotation is a challenging and non-trivial task [9,10,11,12,13,14,15]

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