A comparison of network and clustering methods to detect biogeographical regions

Bioregions are an important concept in biogeography, and are key to our understanding of biodiversity patterns across the world. The use of networks in biogeography to produce bioregions is a relatively novel approach that has been proposed to improve upon current methods. However, it remains unclear if they may be used in place of current methods and/or offer additional biogeographic insights. We compared two network methods to detect bioregions (modularity and map equation) with the conventional distance‐based clustering method. We also explored the relationship between network and biodiversity metrics. For the analysis we used two datasets of iconic Australian plant groups at a continental scale, Acacia and eucalypts, as example groups. The modularity method detected fewer large bioregions produced the most succinct bioregionalisation for both plant groups corresponding to Australian biomes, while map equation detected many small bioregions including interzones at a natural scale of one. The clustering method was less sensitive than network methods in detecting bioregions. The network metric called participation coefficient from both network partition methods identified interzones or transition zones between bioregions. Furthermore, another network metric (betweenness) was highly correlated to richness and endemism. We conclude that the application of networks to biogeography offers a number of advantages and provides novel insights. More specifically, our study showed that these network partition methods were more efficient than the clustering method for bioregionalisation of continental‐scale data in: 1) the identification of bioregions and 2) the quantification of biogeographic transition zones using the participation coefficient metric. The use of network methods and especially the participation coefficient metric adds to bioregionalisation by identifying transition zones which could be useful for conservation purposes and identifying biodiversity hotspots.     

Similarity landscapes: A way to detect many structural and sequence motifs in both introns and exons

When investigators undertake searches of DNA databases, they normally discard large numbers of alignments that demonstrate very weak resemblances to each other, retaining only those that show statistically significant levels of resemblance. We show here that a great deal of information can be extracted from these weak alignments by examining them en masse. This is done by building three-dimensional similarity landscapes from the alignments, landscapes that reveal whether an unusual number of individually nonsignificant alignments tend to match up to a particular region of the query sequence being searched. The power of the search is increased by the use of libraries consisting entirely of introns or of exons. We show that (1) similarity landscapes with a variety of features can be generated from both intron and exon libraries, using introns or exons as query sequences; (2) the landscape features are real and not a statistical artifact; (3) well-known protein motifs used as query sequences can generate various landscape features; and (4) there is some evidence for resemblances between short regions of sequence carried by introns and exons. One possible interpretation of these results is that both introns and exons may have been built up during their evolution from short regions of sequence that as a result are now widely distributed throughout eukaryotic genomes. Such an interpretation would imply that these short regions have common ancestry. Alternatively, the wide sharing of short pieces of DNA may reflect regions with particular structural properties that have arisen through convergent evolution. The similarity-landscape approach can be used to detect such widespread structural motifs and sequence motifs in the genome that might be missed by less-global searches. It can also be used in conjunction with algorithms developed for detecting significant multiple alignments by isolating promising subsets of the databases that can be examined in more detail.Correspondence to: C. Wills     

Palingol: a declarative programming language to describe nucleic acids' secondary structures and to scan sequence database.

At the DNA/RNA level, biological signals are defined by a combination of spatial structures and sequence motifs. Until now, few attempts had been made in writing general purpose search programs that take into account both sequence and structure criteria. Indeed, the most successful structure scanning programs are usually dedicated to particular structures and are written using general purpose programming languages through a complex and time consuming process where the biological problem of defining the structure and the computer engineering problem of looking for it are intimately intertwined. In this paper, we describe a general representation of structures, suitable for database scanning, together with a programming language, Palingol, designed to manipulate it. Palingol has specific data types, corresponding to structural elements-basically helices-that can be arranged in any way to form a complex structure. As a consequence of the declarative approach used in Palingol, the user should only focus on 'what to search for' while the language engine takes care of 'how to look for it'. Therefore, it becomes simpler to write a scanning program and the structural constraints that define the required structure are more clearly identified.     

A quantitative strategy to detect changes in accessibility of protein regions to chemical modification on heterodimerization

We describe a method for studying quantitative changes in accessibility of surface lysine residues of the PB1 subunit of the influenza RNA polymerase as a result of association with the PA subunit to form a PB1‐PA heterodimer. Our method combines two established methods: (i) the chemical modification of surface lysine residues of native proteins by N‐hydroxysuccinimidobiotin (NHS‐biotin) and (ii) the stable isotope labeling of amino acids in cell culture (SILAC) followed by tryptic digestion and mass spectrometry. By linking the chemical modification with the SILAC methodology for the first time, we obtain quantitative data on chemical modification allowing subtle changes in accessibility to be described. Five regions in the PB1 monomer showed altered reactivity to NHS‐biotin when compared with the [PB1‐PA] heterodimer. Mutational analysis of residues in two such regions—at K265 and K481 of PB1, which were about three‐ and twofold, respectively, less accessible to biotinylation in the PB1‐PA heterodimer compared with the PB1 monomer, demonstrated that both K265 and K481 were crucial for polymerase function. This novel assay of quantitative profiling of biotinylation patterns (Q‐POP assay) highlights likely conformational changes at important functional sites, as observed here for PB1, and may provide information on protein–protein interaction interfaces. The Q‐POP assay should be a generally applicable approach and may detect novel functional sites suitable for targeting by drugs.     

Increased coverage obtained by combination of methods for protein sequence database searching

MOTIVATION: Sequence alignment methods that compare two sequences (pairwise methods) are important tools for the detection of biological sequence relationships. In genome annotation, multiple methods are often run and agreement between methods taken as confirmation. In this paper, we assess the advantages of combining search methods by comparing seven pairwise alignment methods, including three local dynamic programming algorithms (PRSS, SSEARCH and SCANPS), two global dynamic programming algorithms (GSRCH and AMPS) and two heuristic approximations (BLAST and FASTA), individually and by pairwise intersection and union of their result lists at equal p-value cut-offs. RESULTS: When applied singly, the dynamic programming methods SCANPS and SSEARCH gave significantly better coverage (p=0.01) compared to AMPS, GSRCH, PRSS, BLAST and FASTA. Results ranked by BLAST p-values gave significantly better coverage compared to ranking by BLAST e-values. Of 56 combinations of eight methods considered, 19 gave significant increases in coverage at low error compared to the parent methods at an equal p-value cutoff. The union of results by BLAST (p-value) and FASTA at an equal p-value cutoff gave significantly better coverage than either method individually. The best overall performance was obtained from the intersection of the results from SSEARCH and the GSRCH62 global alignment method. At an error level of five false positives, this combination found 444 true positives, a significant 12.4% increase over SSEARCH applied alone.     

Protein backbone angle restraints from searching a database for chemical shift and sequence homology

Chemical shifts of backbone atoms in proteins are exquisitely sensitive to local conformation, and homologous proteins show quite similar patterns of secondary chemical shifts. The inverse of this relation is used to search a database for triplets of adjacent residues with secondary chemical shifts and sequence similarity which provide the best match to the query triplet of interest. The database contains 13C, 13C, 13C, 1H and 15N chemical shifts for 20 proteins for which a high resolution X-ray structure is available. The computer program TALOS was developed to search this database for strings of residues with chemical shift and residue type homology. The relative importance of the weighting factors attached to the secondary chemical shifts of the five types of resonances relative to that of sequence similarity was optimized empirically. TALOS yields the 10 triplets which have the closest similarity in secondary chemical shift and amino acid sequence to those of the query sequence. If the central residues in these 10 triplets exhibit similar and backbone angles, their averages can reliably be used as angular restraints for the protein whose structure is being studied. Tests carried out for proteins of known structure indicate that the root-mean-square difference (rmsd) between the output of TALOS and the X-ray derived backbone angles is about 15°. Approximately 3% of the predictions made by TALOS are found to be in error.     

Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology

Chemical shifts of nuclei in or attached to a protein backbone are exquisitely sensitive to their local environment. A computer program, SPARTA, is described that uses this correlation with local structure to predict protein backbone chemical shifts, given an input three-dimensional structure, by searching a newly generated database for triplets of adjacent residues that provide the best match in phi/psi/chi(1 )torsion angles and sequence similarity to the query triplet of interest. The database contains (15)N, (1)H(N), (1)H(alpha), (13)C(alpha), (13)C(beta) and (13)C' chemical shifts for 200 proteins for which a high resolution X-ray (< or =2.4 A) structure is available. The relative importance of the weighting factors for the phi/psi/chi(1) angles and sequence similarity was optimized empirically. The weighted, average secondary shifts of the central residues in the 20 best-matching triplets, after inclusion of nearest neighbor, ring current, and hydrogen bonding effects, are used to predict chemical shifts for the protein of known structure. Validation shows good agreement between the SPARTA-predicted and experimental shifts, with standard deviations of 2.52, 0.51, 0.27, 0.98, 1.07 and 1.08 ppm for (15)N, (1)H(N), (1)H(alpha), (13)C(alpha), (13)C(beta) and (13)C', respectively, including outliers.     

PROF_ PAT 1.3: updated database of patterns used to detect local similarities

MOTIVATION: When analysing novel protein sequences, it is now essential to extend search strategies to include a range of 'secondary' databases. Pattern databases have become vital tools for identifying distant relationships in sequences, and hence for predicting protein function and structure. The main drawback of such methods is the relatively small representation of proteins in trial samples at the time of their construction. Therefore, a negative result of an amino acid sequence comparison with such a databank forces a researcher to search for similarities in the original protein banks. We developed a database of patterns constructed for groups of related proteins with maximum representation of amino acid sequences of SWISS-PROT in the groups. RESULTS: Software tools and a new method have been designed to construct patterns of protein families. By using such method, a new version of databank of protein family patterns, PROF_ PAT 1.3, is produced. This bank is based on SWISS-PROT (r1.38) and TrEMBL (r1.11), and contains patterns of more than 13 000 groups of related proteins in a format similar to that of the PROSITE. Motifs of patterns, which had the minimum level of probability to be found in random sequences, were selected. Flexible fast search program accompanies the bank. The researcher can specify a similarity matrix (the type PAM, BLOSUM and other). Variable levels of similarity can be set (permitting search strategies ranging from exact matches to increasing levels of 'fuzziness'). AVAILABILITY: The Internet address for comparing sequences with the bank is: http://wwwmgs.bionet.nsc.ru/mgs/programs/prof_pat/. The local version of the bank and search programs (approximately 50 Mb) is available via ftp: ftp://ftp.bionet.nsc. ru/pub/biology/vector/prof_pat/, and ftp://ftp.ebi.ac. uk/pub/databases/prof_pat/. Another appropriate way for its external use is to mail amino acid sequences to bachin@vector.nsc.ru for comparison with PROF_ PAT 1.3.     

Identification of similar regions of protein structures using integrated sequence and structure analysis tools

Background  

Understanding protein function from its structure is a challenging problem. Sequence based approaches for finding homology have broad use for annotation of both structure and function. 3D structural information of protein domains and their interactions provide a complementary view to structure function relationships to sequence information. We have developed a web site and an API of web services that enables users to submit protein structures and identify statistically significant neighbors and the underlying structural environments that make that match using a suite of sequence and structure analysis tools. To do this, we have integrated S-BLEST, PSI-BLAST and HMMer based superfamily predictions to give a unique integrated view to prediction of SCOP superfamilies, EC number, and GO term, as well as identification of the protein structural environments that are associated with that prediction. Additionally, we have extended UCSF Chimera and PyMOL to support our web services, so that users can characterize their own proteins of interest.     

Relative importance of chemical attractiveness to parasites for susceptibility to trematode infection

While the host immune system is often considered the most important physiological mechanism against parasites, precontact mechanisms determining exposure to parasites may also affect infection dynamics. For instance, chemical cues released by hosts can attract parasite transmission stages. We used the freshwater snail Lymnaea stagnalis and its trematode parasite Echinoparyphium aconiatum to examine the role of host chemical attractiveness, physiological condition, and immune function in determining its susceptibility to infection. We assessed host attractiveness through parasite chemo‐orientation behavior; physiological condition through host body size, food consumption, and respiration rate; and immune function through two immune parameters (phenoloxidase‐like and antibacterial activity of hemolymph) at an individual level. We found that, although snails showed high variation in chemical attractiveness to E. aconiatum cercariae, this did not determine their overall susceptibility to infection. This was because large body size increased attractiveness, but also increased metabolic activity that reduced overall susceptibility. High metabolic rate indicates fast physiological processes, including immune activity. The examined immune traits, however, showed no association with susceptibility to infection. Our results indicate that postcontact mechanisms were more likely to determine snail susceptibility to infection than variation in attractiveness to parasites. These may include localized immune responses in the target tissue of the parasite. The lack of a relationship between food consumption and attractiveness to parasites contradicts earlier findings that show food deprivation reducing snail attractiveness. This suggests that, although variation in resource level over space and time can alter infection dynamics, variation in chemical attractiveness may not contribute to parasite‐induced fitness variation within populations when individuals experience similar environmental conditions.     

RiceGAAS: an automated annotation system and database for rice genome sequence

An extensive effort of the International Rice Genome Sequencing Project (IRGSP) has resulted in rapid accumulation of genome sequence, and >137 Mb has already been made available to the public domain as of August 2001. This requires a high-throughput annotation scheme to extract biologically useful and timely information from the sequence data on a regular basis. A new automated annotation system and database called Rice Genome Automated Annotation System (RiceGAAS) has been developed to execute a reliable and up-to-date analysis of the genome sequence as well as to store and retrieve the results of annotation. The system has the following functional features: (i) collection of rice genome sequences from GenBank; (ii) execution of gene prediction and homology search programs; (iii) integration of results from various analyses and automatic interpretation of coding regions; (iv) re-execution of analysis, integration and automatic interpretation with the latest entries in reference databases; (v) integrated visualization of the stored data using web-based graphical view. RiceGAAS also has a data submission mechanism that allows public users to perform fully automated annotation of their own sequences. The system can be accessed at http://RiceGAAS.dna.affrc.go.jp/.