Getting the most out of Shannon information

Shannon information is commonly assumed to be the wrong way in which to conceive of information in most biological contexts. Since the theory deals only in correlations between systems, the argument goes, it can apply to any and all causal interactions that affect a biological outcome. Since informational language is generally confined to only certain kinds of biological process, such as gene expression and hormone signalling, Shannon information is thought to be unable to account for this restriction. It is often concluded that a richer, teleosemantic sense of information is needed. I argue against this view, and show that a coherent and sufficiently restrictive theory of biological information can be constructed with Shannon information at its core. This can be done by paying due attention some crucial distinctions: between information quantity and its fitness value, and between carrying information and having the function of doing so. From this I construct an account of how informational functions arise, and show that the “subject matter” of these functions can easily be seen as the natural information dealt with by Shannon’s theory.     

Getting the most out of natural variation in C4 photosynthesis

C₄ photosynthesis is a complex trait that has a high degree of natural variation, involving anatomical and biochemical changes relative to the ancestral C₃ state. It has evolved at least 66 times across a variety of lineages and the evolutionary route from C₃ to C₄ is likely conserved but not necessarily genetically identical. As such, a variety of C₄ species are needed to identify what is fundamental to the C₄ evolutionary process in a global context. In order to identify the genetic components of C₄ form and function, a number of species are used as genetic models. These include Zea mays (maize), Sorghum bicolor (sorghum), Setaria viridis (Setaria), Flaveria bidentis, and Cleome gynandra. Each of these species has different benefits and challenges associated with its use as a model organism. Here, we propose that RNA profiling of a large sampling of C₄, C₃–C₄, and C₃ species, from as many lineages as possible, will allow identification of candidate genes necessary and sufficient to confer C₄ anatomy and/or biochemistry. Furthermore, C₄ model species will play a critical role in the functional characterization of these candidate genes and identification of their regulatory elements, by providing a platform for transformation and through the use of gene expression profiles in mesophyll and bundle sheath cells and along the leaf developmental gradient. Efforts should be made to sequence the genomes of F. bidentis and C. gynandra and to develop congeneric C₃ species as genetic models for comparative studies. In combination, such resources would facilitate discovery of common and unique C₄ regulatory mechanisms across genera.     

Benchmarking PSI-BLAST in genome annotation.

The recognition of remote protein homologies is a major aspect of the structural and functional annotation of newly determined genomes. Here we benchmark the coverage and error rate of genome annotation using the widely used homology-searching program PSI-BLAST (position-specific iterated basic local alignment search tool). This study evaluates the one-to-many success rate for recognition, as often there are several homologues in the database and only one needs to be identified for annotating the sequence. In contrast, previous benchmarks considered one-to-one recognition in which a single query was required to find a particular target. The benchmark constructs a model genome from the full sequences of the structural classification of protein (SCOP) database and searches against a target library of remote homologous domains (<20 % identity). The structural benchmark provides a reliable list of correct and false homology assignments. PSI-BLAST successfully annotated 40 % of the domains in the model genome that had at least one homologue in the target library. This coverage is more than three times that if one-to-one recognition is evaluated (11 % coverage of domains). Although a structural benchmark was used, the results equally apply to just sequence homology searches. Accordingly, structural and sequence assignments were made to the sequences of Mycoplasma genitalium and Mycobacterium tuberculosis (see http://www.bmm.icnet. uk). The extent of missed assignments and of new superfamilies can be estimated for these genomes for both structural and functional annotations.     

Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments

Sequence alignments may be the most fundamental computational resource for molecular biology. The best methods that identify sequence relatedness through profile-profile comparisons are much slower and more complex than sequence-sequence and sequence-profile comparisons such as, respectively, BLAST and PSI-BLAST. Families of related genes and gene products (proteins) can be represented by consensus sequences that list the nucleic/amino acid most frequent at each sequence position in that family. Here, we propose a novel approach for consensus-sequence-based comparisons. This approach improved searches and alignments as a standard add-on to PSI-BLAST without any changes of code. Improvements were particularly significant for more difficult tasks such as the identification of distant structural relations between proteins and their corresponding alignments. Despite the fact that the improvements were higher for more divergent relations, they were consistent even at high accuracy/low error rates for non-trivially related proteins. The improvements were very easy to achieve; no parameter used by PSI-BLAST was altered and no single line of code changed. Furthermore, the consensus sequence add-on required relatively little additional CPU time. We discuss how advanced users of PSI-BLAST can immediately benefit from using consensus sequences on their local computers. We have also made the method available through the Internet (http://www.rostlab.org/services/consensus/).     

Getting the most out of waste: how dung beetles boost the nitrogen content in their food

The life cycle of almost all dung beetles revolve around mammalian dung, the feed on dung, look for mating partners on dung and lay eggs in the dung. We know they feed on dung, but we still do not understand how exactly they filter‐feed on the dung and which particles size range they are ingesting. The aim on this study was to investigate the filter feeding by particle selection by adult dung beetles using Scarabaeus goryi and how that improves the nutrient quality of the ingested particles. We compared the particle sizes and nutrient content of the dung with the ingested material in the foregut, hindgut and the faeces of the dung beetle. Adult dung beetles do select smaller dung particles when feeding, we found the maximum particle size for the ingested particle to be around 1400 μm. The average particle size increased through the gut length. Dung beetles also selected particles with higher nitrogen content when feeding, the nitrogen content increased from about 1.5% in the dung to just over 5% in the foregut which then decreased to the level of the unprocessed dung in the dung beetle faeces. Carbon content did not increase from the unprocessed dung to the foregut but decreased through the gut length. Feeding by particle size selection by dung beetles helps in selecting particles with higher nitrogen content to compensate for the low levels found in dung.     

Protein subcellular localization based on PSI-BLAST and machine learning

Subcellular location is an important functional annotation of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is necessary for large-scale genome analysis. This paper describes a protein subcellular localization method which extracts features from protein profiles rather than from amino acid sequences. The protein profile represents a protein family, discards part of the sequence information that is not conserved throughout the family and therefore is more sensitive than the amino acid sequence. The amino acid compositions of whole profile and the N-terminus of the profile are extracted, respectively, to train and test the probabilistic neural network classifiers. On two benchmark datasets, the overall accuracies of the proposed method reach 89.1% and 68.9%, respectively. The prediction results show that the proposed method perform better than those methods based on amino acid sequences. The prediction results of the proposed method are also compared with Subloc on two redundance-reduced datasets.     

Evaluation of PSI-BLAST alignment accuracy in comparison to structural alignments

The PSI-BLAST algorithm has been acknowledged as one of the most powerful tools for detecting remote evolutionary relationships by sequence considerations only. This has been demonstrated by its ability to recognize remote structural homologues and by the greatest coverage it enables in annotation of a complete genome. Although recognizing the correct fold of a sequence is of major importance, the accuracy of the alignment is crucial for the success of modeling one sequence by the structure of its remote homologue. Here we assess the accuracy of PSI-BLAST alignments on a stringent database of 123 structurally similar, sequence-dissimilar pairs of proteins, by comparing them to the alignments defined on a structural basis. Each protein sequence is compared to a nonredundant database of the protein sequences by PSI-BLAST. Whenever a pair member detects its pair-mate, the positions that are aligned both in the sequential and structural alignments are determined, and the alignment sensitivity is expressed as the percentage of these positions out of the structural alignment. Fifty-two sequences detected their pair-mates (for 16 pairs the success was bi-directional when either pair member was used as a query). The average percentage of correctly aligned residues per structural alignment was 43.5+/-2.2%. Other properties of the alignments were also examined, such as the sensitivity vs. specificity and the change in these parameters over consecutive iterations. Notably, there is an improvement in alignment sensitivity over consecutive iterations, reaching an average of 50.9+/-2.5% within the five iterations tested in the current study.     

PSI protein classifier: A new program automating PSI-BLAST search results

A new program, PSI Protein Classifier, generalizing the results of both successive and independent iterations of the PSI-BLAST program was developed. The technical opportunities of the program are described and illustrated by two examples. An iterative screening of the amino acid sequence database detected potential evolutionary relationships between GH5, GH13, GH27, GH31, GH36, GH66, GH101 and GH114 families of glycoside hydrolases. Analysis of the statistically significant sequence similarity (E-value analysis) allowed us to divide the family GH31 into 38 subfamilies.     

BeoBLAST: distributed BLAST and PSI-BLAST on a Beowulf cluster

BeoBLAST is an integrated software package that handles user requests and distributes BLAST and PSI-BLAST searches to nodes of a Beowulf cluster, thus providing a simple way to implement a scalable BLAST system on top of relatively inexpensive computer clusters. Additionally, BeoBLAST offers a number of novel search features through its web interface, including the ability to perform simultaneous searches of multiple databases with multiple queries, and the ability to start a search using the PSSM generated from a previous PSI-BLAST search on a different database. The underlying system can also handle automated querying for high throughput work. AVAILABILITY: Source code is available under the GNU public license at http://bioinformatics.fccc.edu/     

Protein domain identification and improved sequence similarity searching using PSI-BLAST

Protein sequences containing more than one structural domain are problematic when used in homology searches where they can either stop an iterative database search prematurely or cause an explosion of a search to common domains. We describe a method, DOMAINATION, that infers domains and their boundaries in a query sequence from local gapped alignments generated using PSI-BLAST. Through a new technique to recognize domain insertions and permutations, DOMAINATION submits delineated domains as successive database queries in further iterative steps. Assessed over a set of 452 multidomain proteins, the method predicts structural domain boundaries with an overall accuracy of 50% and improves finding distant homologies by 14% compared with PSI-BLAST. DOMAINATION is available as a web based tool at http://mathbio.nimr.mrc.ac.uk, and the source code is available from the authors upon request.