The fate of Arabidopsis thaliana homeologous CNSs and their motifs in the Paleohexaploid Brassica rapa.

Following polyploidy, duplicate genes are often deleted, and if they are not, then duplicate regulatory regions are sometimes lost. By what mechanism is this loss and what is the chance that such a loss removes function? To explore these questions, we followed individual Arabidopsis thaliana-A. thaliana conserved noncoding sequences (CNSs) into the Brassica ancestor, through a paleohexaploidy and into Brassica rapa. Thus, a single Brassicaceae CNS has six potential orthologous positions in B. rapa; a single Arabidopsis CNS has three potential homeologous positions. We reasoned that a CNS, if present on a singlet Brassica gene, would be unlikely to lose function compared with a more redundant CNS, and this is the case. Redundant CNSs go nondetectable often. Using this logic, each mechanism of CNS loss was assigned a metric of functionality. By definition, proved deletions do not function as sequence. Our results indicated that CNSs that go nondetectable by base substitution or large insertion are almost certainly still functional (redundancy does not matter much to their detectability frequency), whereas those lost by inferred deletion or indels are approximately 75% likely to be nonfunctional. Overall, an average nondetectable, once-redundant CNS more than 30 bp in length has a 72% chance of being nonfunctional, and that makes sense because 97% of them sort to a molecular mechanism with deletion in its description, but base substitutions do cause loss. Similarly, proved-functional G-boxes go undetectable by deletion 82% of the time. Fractionation mutagenesis is a procedure that uses polyploidy as a mutagenic agent to genetically alter RNA expression profiles, and then to construct testable hypotheses as to the function of the lost regulatory site. We show fractionation mutagenesis to be a deletion machine in the Brassica lineage

The effect of primer choice and short read sequences on the outcome of 16S rRNA gene based diversity studies

Different regions of the bacterial 16S rRNA gene evolve at different evolutionary rates. The scientific outcome of short read sequencing studies therefore alters with the gene region sequenced. We wanted to gain insight in the impact of primer choice on the outcome of short read sequencing efforts. All the unknowns associated with sequencing data, i.e. primer coverage rate, phylogeny, OTU-richness and taxonomic assignment, were therefore implemented in one study for ten well established universal primers (338f/r, 518f/r, 799f/r, 926f/r and 1062f/r) targeting dispersed regions of the bacterial 16S rRNA gene. All analyses were performed on nearly full length and in silico generated short read sequence libraries containing 1175 sequences that were carefully chosen as to present a representative substitute of the SILVA SSU database. The 518f and 799r primers, targeting the V4 region of the 16S rRNA gene, were found to be particularly suited for short read sequencing studies, while the primer 1062r, targeting V6, seemed to be least reliable. Our results will assist scientists in considering whether the best option for their study is to select the most informative primer, or the primer that excludes interferences by host-organelle DNA. The methodology followed can be extrapolated to other primers, allowing their evaluation prior to the experiment

Predictive motifs derived from cytosine methyltransferases

Thirteen bacterial DNA methyltransferases that catalyze the formation of 5-methylcytosine within specific DNA sequences possess related structures. Similar building blocks (motifs), containing invariant positions, can be found in the same order in all thirteen sequences. Five of these blocks are highly conserved while a further five contain weaker similarities. One block, which has the most invariant residues, contains the proline-cysteine dipeptide of the proposed catalytic site. A region in the second half of each sequence is unusually variable both in length and sequence composition. Those methyltransferases that exhibit significant homology in this region share common specificity in DNA recognition. The five highly conserved motifs can be used to discriminate the known 5-methylcytosine forming methyltransferases from all other methyltransferases of known sequence, and from all other identified proteins in the PIR, GenBank and EMBL databases. These five motifs occur in a mammalian methyltransferase responsible for the formation of 5-methylcytosine within CG dinucleotides. By searching the unidentified open reading frames present in the GenBank and EMBL databases, two potential 5-methylcytosine forming methyltransferases have been found

Alignment of helical membrane protein sequences using AlignMe

Few sequence alignment methods have been designed specifically for integral membrane proteins, even though these important proteins have distinct evolutionary and structural properties that might affect their alignments. Existing approaches typically consider membrane-related information either by using membrane-specific substitution matrices or by assigning distinct penalties for gap creation in transmembrane and non-transmembrane regions. Here, we ask whether favoring matching of predicted transmembrane segments within a standard dynamic programming algorithm can improve the accuracy of pairwise membrane protein sequence alignments. We tested various strategies using a specifically designed program called AlignMe. An updated set of homologous membrane protein structures, called HOMEP2, was used as a reference for optimizing the gap penalties. The best of the membrane-protein optimized approaches were then tested on an independent reference set of membrane protein sequence alignments from the BAliBASE collection. When secondary structure (S) matching was combined with evolutionary information (using a position-specific substitution matrix (P)), in an approach we called AlignMePS, the resultant pairwise alignments were typically among the most accurate over a broad range of sequence similarities when compared to available methods. Matching transmembrane predictions (T), in addition to evolutionary information, and secondary-structure predictions, in an approach called AlignMePST, generally reduces the accuracy of the alignments of closely-related proteins in the BAliBASE set relative to AlignMePS, but may be useful in cases of extremely distantly related proteins for which sequence information is less informative. The open source AlignMe code is available at https://sourceforge.net/projects/alignme​/, and at http://www.forrestlab.org, along with an online server and the HOMEP2 data set

Mrub_3029, Mrub_2052, are predicted orthologs of b_0688, b_0394, while Mrub_0759 and Mrub_2365 are not predicted orthologs of b_1309, in \u3cem\u3eEscherichia coli\u3c/em\u3e, which code for enzymes involved in starch and sucrose metabolism

We predict that Mrub__[0759] encodes the enzyme [Meiothermus ruber Fruktokinase] (DNA coordinates [741282..742202 on the forward strand] which is the 00500 step of the Starch and Sucrose Metabolism pathway (KEGG map number [2.7.1.4]). It catalyzes the conversion of [ATP + D-fructoseADP + D-fructose 6-phosphate]. The E. coli K12 MG1655 ortholog is predicted to be b1309, which has the gene identifier [ycjM] We predict that Mrub__[ 2365] encodes the enzyme [Meiothermus ruber Fruktokinase] (DNA coordinates [2417118..2418059 on the forward strand], which is the [00500] step of the [Starch and Sucrose Metabolism] pathway (KEGG map number [2.7.1.4]). It catalyzes the conversion of [ATP + D-fructoseADP + D-fructose 6-phosphate]. The E. coli K12 MG1655 ortholog is predicted to be b1309, which has the gene identifier [ycjM]. We predict that Mrub__[ 3029] encodes the enzyme [Meiothermus ruber Sucrose phosphorylase] (DNA coordinates [3072410..3074080 on the forward strand]), which is the [00500] step of the [Starch and Sucrose Metabolism] pathway (KEGG map number [2.4.1.7 ]). It catalyzes the conversion of [sucrose + phosphate → β-D-fructofuranose + α-D-glucopyranose 1-phosphate]. The E. coli K12 MG1655 ortholog is predicted to be b0688, which has the gene identifier [pgm]. We predict that Mrub__[ 2052] encodes the enzyme [Meiothermus ruber phosphoglucomutase] (DNA coordinates [2088542..2090185 on the reverse strand], which is the [00500] step of the [Starch and Sucrose Metabolism] pathway (KEGG map number [5.4.2.2]). It catalyzes the conversion of [Alpha-D-glucose 1-phosphatealpha-D-glucose 6-phosphate]. The E. coli K12 MG1655 ortholog is predicted to be b0394, which has the gene identifier [mak]

A data science approach to pattern discovery in complex structures with applications in bioinformatics

Pattern discovery aims to find interesting, non-trivial, implicit, previously unknown and potentially useful patterns in data. This dissertation presents a data science approach for discovering patterns or motifs from complex structures, particularly complex RNA structures. RNA secondary and tertiary structure motifs are very important in biological molecules, which play multiple vital roles in cells. A lot of work has been done on RNA motif annotation. However, pattern discovery in RNA structure is less studied. In the first part of this dissertation, an ab initio algorithm, named DiscoverR, is introduced for pattern discovery in RNA secondary structures. This algorithm works by representing RNA secondary structures as ordered labeled trees and performs tree pattern discovery using a quadratic time dynamic programming algorithm. The algorithm is able to identify and extract the largest common substructures from two RNA molecules of different sizes, without prior knowledge of locations and topologies of these substructures. One application of DiscoverR is to locate the RNA structural elements in genomes. Experimental results show that this tool complements the currently used approaches for mining conserved structural RNAs in the human genome. DiscoverR can also be extended to find repeated regions in an RNA secondary structure. Specifically, this extended method is used to detect structural repeats in the 3\u27-untranslated region of a protein kinase gene

Do RNA-dependent polymerases share common ancestry? A bioinformatic approach

Bioinformatics is the use of computational methods to perform hypothesis-driven research that generates new knowledge from existing biological databases. For any bioinformatic analysis, it is important that the most accurate method(s) be used. The first portion of this thesis is a comparative evaluation of six programs designed for the local alignment of protein sequences. The results demonstrate that two of the programs, MEME and PROBE, outperform all other programs (BLOCKMAKER, ITERALIGN, MATCHBOX, and PIMA). The second portion of this thesis uses MEME and PROBE in an attempt to locate an ordered-series-of-motifs (OSM) among two groups of RNA-dependent polymerases, the large (L) protein from viruses in the order Mononegavirales and the reverse transcriptase (RT) protein from retroviruses and retroid agents. An OSM was not detected among the L and RT proteins, suggesting that they are not homologs. This result also supports the hypothesis that all RNA-dependent polymerases do not share common ancestry

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

PSI-BLAST-ISS: an intermediate sequence search tool for estimation of the position-specific alignment reliability

BACKGROUND: Protein sequence alignments have become indispensable for virtually any evolutionary, structural or functional study involving proteins. Modern sequence search and comparison methods combined with rapidly increasing sequence data often can reliably match even distantly related proteins that share little sequence similarity. However, even highly significant matches generally may have incorrectly aligned regions. Therefore when exact residue correspondence is used to transfer biological information from one aligned sequence to another, it is critical to know which alignment regions are reliable and which may contain alignment errors. RESULTS: PSI-BLAST-ISS is a standalone Unix-based tool designed to delineate reliable regions of sequence alignments as well as to suggest potential variants in unreliable regions. The region-specific reliability is assessed by producing multiple sequence alignments in different sequence contexts followed by the analysis of the consistency of alignment variants. The PSI-BLAST-ISS output enables the user to simultaneously analyze alignment reliability between query and multiple homologous sequences. In addition, PSI-BLAST-ISS can be used to detect distantly related homologous proteins. The software is freely available at: . CONCLUSION: PSI-BLAST-ISS is an effective reliability assessment tool that can be useful in applications such as comparative modelling or analysis of individual sequence regions. It favorably compares with the existing similar software both in the performance and functional features