The missing wetlands: using local ecological knowledge to find cryptic ecosystems

Small, temporally dynamic, biologically diverse isolated wetlands are among the most imperiled ecosystems, yet their conservation is hindered by lack of protective legislation and mapping. As part of an effort to better understand isolated wetland ecology in an area undergoing dramatic land use change, we mapped isolated wetlands in South Carolina’s Piedmont and Blue Ridge regions using remote sensing and local ecological knowledge (LEK). Remote detection of isolated wetlands was limited by digital resource resolution, topography, and wetland size. LEK was the most useful tool for locating small isolated wetlands. We sampled 10% of the study area using LEK and discovered 44 wetlands with “isolated” characteristics, none of which had been identified by remote sensing. Only 8 of 44 wetlands found through LEK could be identified using remote sensing after their discovery. LEK fills a gap in cryptic ecosystem detection when adequate remotely sensed data are not available. Though effective, using LEK is neither as rapid nor as repeatable as remote sensing. We suggest a two-pronged approach for finding cryptic ecosystems: remote sensing coupled with LEK where data resolution is inadequate. For remote detection of isolated wetlands, we suggest a minimum resolution of 0.33 m for Color Infrared, leaf-off, high-water photography. Despite great advances in remote sensing, data are not uniformly available worldwide and LEK may serve as an effective tool for locating cryptic resources for biodiversity conservation. Mapping cryptic resources will allow for more accurate resource and biodiversity conservation planning under current and future climate scenarios.     

DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules

Background  

Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns.     

GS-Finder: a program to find bacterial gene start sites with a self-training method

In this paper, a self-training method is proposed to recognize translation start sites in bacterial genomes without a prior knowledge of rRNA in the genomes concerned. Many features with biological meanings are incorporated, including mononucleotide distribution patterns near the start codon, the start codon itself, the coding potential and the distance from the most-left start codon to the start codon. The proposed method correctly predicts 92% of the translation start sites of 195 experimentally confirmed Escherichia coli CDSs, 96% of 58 reliable Bacillus subtilis CDSs and 82% of 140 reliable Synechocystis CDSs. Moreover, the self-training method presented might also be used to relocate the translation start sites of putative CDSs of genomes, which are predicted by gene-finding programs. After post-processing by the method presented, the improvement of gene start prediction of some gene-finding programs is remarkable, e.g., the accuracy of gene start prediction of Glimmer 2.02 increases from 63 to 91% for 832 E. coli reliable CDSs. An open source computer program to implement the method, GS-Finder, is freely available for academic purposes from http://tubic.tju.edu.cn/GS-Finder/.     

smORFer: a modular algorithm to detect small ORFs in prokaryotes

Emerging evidence places small proteins (≤50 amino acids) more centrally in physiological processes. Yet, their functional identification and the systematic genome annotation of their cognate small open-reading frames (smORFs) remains challenging both experimentally and computationally. Ribosome profiling or Ribo-Seq (that is a deep sequencing of ribosome-protected fragments) enables detecting of actively translated open-reading frames (ORFs) and empirical annotation of coding sequences (CDSs) using the in-register translation pattern that is characteristic for genuinely translating ribosomes. Multiple identifiers of ORFs that use the 3-nt periodicity in Ribo-Seq data sets have been successful in eukaryotic smORF annotation. They have difficulties evaluating prokaryotic genomes due to the unique architecture (e.g. polycistronic messages, overlapping ORFs, leaderless translation, non-canonical initiation etc.). Here, we present a new algorithm, smORFer, which performs with high accuracy in prokaryotic organisms in detecting putative smORFs. The unique feature of smORFer is that it uses an integrated approach and considers structural features of the genetic sequence along with in-frame translation and uses Fourier transform to convert these parameters into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way, and dependent on the data available for a particular organism, different modules can be selected for smORF search.     

A rapid method of gene detection using DNA bound to Sephacryl

A rapid method of gene detection has been developed utilising DNA fragments immobilized on resins and a sandwich hybridization assay. This method permits the detection of restriction fragment length polymorphisms (RFLPs) without the need to immobilize sample DNA. The method is based on the use of two non-overlapping DNA restriction fragments, one of which is attached to a resin (fragment A) and the other 32P-labelled (fragment B). Fragments A and B will not hybridize to each other unless there is a DNA or RNA fragment capable of hybridizing to both A and B present in the same reaction. Hybridization in this instance will result in the resin being radioactively labelled. The RFLP associated with the mutation causing sickle-cell anaemia was used as a model to develop the method. The resin Sephacryl S-500 appeared most suited to our method for two reasons: (i) DNA immobilization experiments using two coupling procedures and four resins indicated that Sephacryl S-500 bound the most DNA with very little non-covalent coupling. (ii) Hybridization experiments with DNA bound to a number of resins showed that DNA bound to Sephacryl S-500 hybridized most efficiently with a low level of nonspecific hybridization. Using optimum hybridization conditions 5 X 10(-18) mol of beta-globin DNA could be detected. The method has been used to distinguish between DNA from sickle, heterozygote and normal patients.     

PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer

MOTIVATION: Elucidating the molecular taxonomy of cancers and finding biological and clinical markers from microarray experiments is problematic due to the large number of variables being measured. Feature selection methods that can identify relevant classifiers or that can remove likely false positives prior to supervised analysis are therefore desirable. RESULTS: We present a novel feature selection procedure based on a mixture model and a non-gaussianity measure of a gene's expression profile. The method can be used to find genes that define either small outlier subgroups or major subdivisions, depending on the sign of kurtosis. The method can also be used as a filtering step, prior to supervised analysis, in order to reduce the false discovery rate. We validate our methodology using six independent datasets by rediscovering major classifiers in ER negative and ER positive breast cancer and in prostate cancer. Furthermore, our method finds two novel subtypes within the basal subgroup of ER negative breast tumours, associated with apoptotic and immune response functions respectively, and with statistically different clinical outcome. AVAILABILITY: An R-function pack that implements the methods used here has been added to vabayelMix, available from (www.cran.r-project.org). CONTACT: aet21@cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.     

Semi-quantitative detection of gene expression using bisbenzimide dye

An electrochemical biosensor, using a disposable electrochemical printed chip aggregation by the bisbenzimide dye (Hoechst 33258), was used for detecting the expression of β-actin and RAGE genes. Using linear sweep voltammetry, the expression of these two genes in HeLa and HepG2 cell lines was determined based on anodic peak current, and the results were compared with conventional agarose gel electrophoresis. Total cellular RNA was reverse transcribed to complementary DNA, and amplification by PCR was carried out. Subsequently, the PCR products were subjected to detection by either electrophoresis or electrochemical biosensor. Precision of the electrochemical biosensor technique was acceptable (β-actin: CV = 1.875% for 10(4) copies and 4.684% for 10(9) copies; RAGE: CV = 2.253% for 10(9) copies, and 3.743% for 10 copies). In the electrochemical biosensor technique, the PCR products were measured in the same run with various concentrations of standards, and copy numbers of β-actin gene were interpolated from a standard curve. Copy numbers of the β-actin gene were then compared between the two techniques. At the 95% confidence limit, the two methods had no significant differences and were significantly correlated (y = -40383.0623 + 1.0233x; P > 0.10). The electrochemical biosensor method was more sensitive than the conventional electrophoresis method because it could detect as low as 10 copies of the RAGE gene. The conventional electrophoresis method detected the RAGE gene at concentrations of at least 10(4) copies, and the linearity for semi-quantitative measurement was in the range of 10(6)-10(9) copies. When the electrochemical biosensor was applied to detect the RAGE gene expression in both cell types, we found that HeLa cells expressed the RAGE gene about 2-fold higher than in HepG2 cells (relative value of 0.000905 vs 0.0004670). Therefore, this study suggests the potential modification of the electrochemical biosensor with the use of bisbenzimide dye (Hoechst 33258) for detecting gene expression.     

Algorithm to find gene expression profiles of deregulation and identify families of disease-altered genes

MOTIVATION: Alteration of gene expression often results in up- or down-regulated genes and the most common analysis strategies look for such differentially expressed genes. However, molecular disease mechanisms typically constitute abnormalities in the regulation of genes producing strong alterations in the expression levels. The search for such deregulation states in the genomic expression profiles will help to identify disease-altered genes better. RESULTS: We have developed an algorithm that searches for the genes which present a significant alteration in the variability of their expression profiles, by comparing an altered state with a control state. The algorithm provides groups of genes and assigns a statistical measure of significance to each group of genes selected. The method also includes a prefilter tool to select genes with a threshold of differential expression that can be set by the user ad casum. The method is evaluated using an experimental set of microarrays of human control and cancer samples from patients with acute promyelocytic leukemia.     

EGASP: collaboration through competition to find human genes

According to the most recent estimates, the number of human genes is possibly--but not certainly--between 20,000 and 25,000. To contribute strategies to reduce this uncertainty, several groups working on computational gene prediction met recently at the Welcome Trust Sanger Institute with the goal to test and compare predictive methods of genome annotation.     

MARS: a program to find potential restriction sites

The figure shown below should have accompanied the paper detailedhere.     

New features of Asian Crassostrea oyster mitochondrial genomes: A novel alloacceptor tRNA gene recruitment and two novel ORFs

A feasible way to perform evolutionary analyses is to compare characters divergent enough to observe significant differences, but sufficiently similar to exclude saturation of the differences that occurred. Thus, comparisons of invertebrate mitochondrial (mt) genomes at low taxonomic levels can be extremely helpful in investigating patterns of variation and evolutionary dynamics of genomes, as intermediate stages of the process may be identified. Fortunately, in this study, we newly sequenced the mt genome of the eighth member of Asian Crassostrea oysters which can provide necessary intermediate characters for us to believe that the variation of Crassostrea mt genomes is considerably greater than previously acknowledged. Several new features of Asian Crassostrea oyster mitochondrial genomes were revealed, and our results are particularly significant as they 1) suggest a novel model of alloacceptor tRNA gene recruitment, namely "vertical" tRNA gene recruitment, which can be successfully used to explain the origination of the unusually additional trnK and trnQ genes (annotated as trnK(2) and trnQ(2) respectively) in the mt genomes of the five Asian oysters, and we speculate that this recruitment progress may be a common phenomenon in the evolution of the tRNA multigene family; 2) reveal the existence of two additional, lineage-specific, mtDNA-encoded genes that may originate from duplication of nad2 followed by rapid evolutionary change. Each of these two genes encodes a unique amino terminal signal peptide, thus each might possess an unknown function; and 3) identify for the first time the atp8 gene in oysters. The present study thus gives further credence to the comparison of congeneric bivalves as a meaningful strategy to investigate mt genomic evolutionary trends in genome organization, tRNA multigene family, and gene loss and/or duplication that are difficult to undertake at higher taxonomic levels. In particular, our study provides new evidence for the identification and characterization of ORFs in the "non-coding region" of animal mt genomes.     

A program to find regions of similarity between homologous protein sequences using dotmatrix analysis

MATRIX is a program designed primarily to enable the user to visualize all regions of similarity between two proteins at a glance. The program helps the user to see where they are similar—at what relative positions in the amino acid sequences of the two proteins in question does the similarity exist; how they are similar—what functional characteristics the two similar sequences have in common; and to what extent they are similar—is the similarity significant, if so how significant relative to other similar sequences in the protein. This is achieved by constructing a diagram in which quantitative parameters of amino acids are used to compare every amino acid residue of the first protein with every amino acid residue of the second.Another function of the program is, given two sets of atomic coordinates—either of different proteins or for the same protein (for self-comparison)—to demonstrate which residues of the two proteins, when the two proteins are superimposed upon each other, appear in the same space (or are close to each other).