Correction: Benchmarking tools for the alignment of functional noncoding DNA

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.AbstractIn follow-up studies to this work [1], we have identified an error in a single line of code responsible for parsing BLASTZ [2] alignments that affects our previously published results for this alignment tool. This error resulted in a reduction in overall alignment coverage, with a concomitant underestimation of alignment sensitivity and overestimation of alignment specificity. As BLASTZ is an important and widely used alignment tool, we present here the revised results of our performance evaluations for BLASTZ together with previously reported results for the other alignment tools studied, which have been subsequently verified (Figures 1-4). The general conclusions presented in [1] remain unchanged, although the following sections concerning BLASTZ performance must be modified in light of our recent findings. The true overall alignment coverage for BLASTZ with and without insertion/deletion evolution and with and without blocks of constraint is shown in Figure 1, and reveals increased overall coverage in the presence of constrained blocks for intermediate to high divergence distances (Figures 1C & 1D) relative to previous results ([1] Figures 3C & 3D). As a consequence, the true overall sensitivity for BLASTZ is increased for intermediate to high divergence distances, especially in the presence of insertion/deletion evolution and constrained blocks (Figure 2D) relative to previous results ([1] Figure 4D). The most important revisions to [1] concern BLASTZ performance in interspersed blocks of constrained sequences (Figures 3, 4). Figure 3 shows that the true constraint coverage, and therefore constraint sensitivity, of BLASTZ is much improved relative to previous results for intermediate to high divergence distances ([1], Figure 5). Thus BLASTZ has increased constraint coverage relative to overall coverage (cp. Figures 1C & 1D with 3A & 3B), indicating that BLASTZ local alignments preferentially occur in constrained sequences for intermediate to high divergence distances, overturning claims on page 6 of [1] to the contrary. Likewise, the claim that BLASTZ has a "dramatic decrease in constraint sensitivity in the presence of indel evolution" on page 10 of [1] is incorrect. The increase in overall coverage, however, decreases the constraint specificity of BLASTZ for intermediate to high divergence distances (Figure 4A & 4B) relative to previous results ([1] Figure 6A & 6B). This decrease in constraint specificity requires reconsideration of the use of BLASTZ local alignments as specific detectors of constrained noncoding sequences discussed page 10 of [1]. Revised performance statistics for BLASTZ are posted along with previous results at [3]. We apologize for any misconception or inconvenience this error may have caused. References: 1. Pollard DA, Bergman CM, Stoye J, Celniker SE, Eisen MB: Benchmarking tools for the alignment of functional noncoding DNA. BMC Bioinformatics 2004, 5:6. 2. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W: Human-mouse alignments with BLASTZ. Genome Res 2003, 13:103-7. 3. AlignmentBenchmarking [http://rana.lbl.gov/AlignmentBenchmarking]Peer Reviewe

Computational analysis of human genomic variants and lncRNAs from sequence data

The high-throughput sequencing technologies have been developed and applied to the human genome studies for nearly 20 years. These technologies have provided numerous research applications and have significantly expanded our knowledge about the human genome. In this thesis, computational methods that utilize sequence data to study human genomic variants and transcripts were evaluated and developed. Indel represents insertion and deletion, which are two types of common genomic variants that are widespread in the human genome. Detecting indels from human genomes is the crucial step for diagnosing indel related genomic disorders and may potentially identify novel indel makers for studying certain diseases. Compared with previous techniques, the high-throughput sequencing technologies, especially the next- generation sequencing (NGS) technology, enable to detect indels accurately and efficiently in wide ranges of genome. In the first part of the thesis, tools with indel calling abilities are evaluated with an assortment of indels and different NGS settings. The results show that the selection of tools and NGS settings impact on indel detection significantly, which provide suggestions for tool selection and future developments. In bioinformatics analysis, an indel’s position can be marked inconsistently on the reference genome, which may result in an indel having different but equivalent representations and cause troubles for downstream. This problem is related to the complex sequence context of the indels, for example, short tandem repeats (STRs), where the same short stretch of nucleotides is amplified. In the second part of the thesis, a novel computational tool VarSCAT was described, which has various functions for annotating the sequence context of variants, including ambiguous positions, STRs, and other sequence context features. Analysis of several high- confidence human variant sets with VarSCAT reveals that a large number of genomic variants, especially indels, have sequence features associated with STRs. In the human genome, not all genes and their transcripts are translated into proteins. Long non-coding ribonucleic acid (lncRNA) is a typical example. Sequence recognition built with machine learning models have improved significantly in recent years. In the last part of the thesis, several machine learning-based lncRNA prediction tools were evaluated on their predictions for coding potentiality of transcripts. The results suggest that tools based on deep learning identify lncRNAs best. Ihmisen genomivarianttien ja lncRNA:iden laskennallinen analyysi sekvenssiaineistosta Korkean suorituskyvyn sekvensointiteknologioita on kehitetty ja sovellettu ihmisen genomitutkimuksiin lähes 20 vuoden ajan. Nämä teknologiat ovat mahdollistaneet ihmisen genomin laaja-alaisen tutkimisen ja lisänneet merkittävästi tietoamme siitä. Tässä väitöstyössä arvioitiin ja kehitettiin sekvenssiaineistoa hyödyntäviä laskennallisia menetelmiä ihmisen genomivarianttien sekä transkriptien tutkimiseen. Indeli on yhteisnimitys lisäys- eli insertio-varianteille ja häviämä- eli deleetio-varianteille, joita esiintyy koko genomin alueella. Indelien tunnistaminen on ratkaisevaa geneettisten poikkeavuuksien diagnosoinnissa ja eri sairauksiin liittyvien uusien indeli-markkereiden löytämisessä. Aiempiin teknologioihin verrattuna korkean suorituskyvyn sekvensointiteknologiat, erityisesti seuraavan sukupolven sekvensointi (NGS) mahdollistavat indelien havaitsemisen tarkemmin ja tehokkaammin laajemmilta genomialueilta. Väitöstyön ensimmäisessä osassa indelien kutsumiseen tarkoitettuja laskentatyökaluja arvioitiin käyttäen laajaa valikoimaa indeleitä ja erilaisia NGS-asetuksia. Tulokset osoittivat, että työkalujen valinta ja NGS-asetukset vaikuttivat indelien tunnistukseen merkittävästi ja siten ne voivat ohjata työkalujen valinnassa ja kehitystyössä. Bioinformatiivisessa analyysissä saman indelin sijainti voidaan merkitä eri kohtiin referenssigenomia, joka voi aiheuttaa ongelmia loppupään analyysiin, kuten indeli-kutsujen arviointiin. Tämä ongelma liittyy sekvenssikontekstiin, koska variantit voivat sijoittua lyhyille perättäisille tandem-toistojaksoille (STR), jossa sama lyhyt nukleotidijakso on monistunut. Väitöstyön toisessa osassa kehitettiin laskentatyökalu VarSCAT, jossa on eri toimintoja, mm. monitulkintaisten sijaintitietojen, vierekkäisten alueiden ja STR-alueiden tarkasteluun. Luotettaviksi arvioitujen ihmisen varianttiaineistojen analyysi VarSCAT-työkalulla paljasti, että monien geneettisten varianttien ja erityisesti indelien ominaisuudet liittyvät STR-alueisiin. Kaikkia ihmisen geenejä ja niiden geenituotteita, kuten esimerkiksi ei-koodaavia RNA:ta (lncRNA) ei käännetä proteiiniksi. Koneoppimismenetelmissä ja sekvenssitunnistuksessa on tapahtunut huomattavaa parannusta viime vuosina. Väitöstyön viimeisessä osassa arvioitiin useiden koneoppimiseen perustuvien lncRNA-ennustustyökalujen ennusteita. Tulokset viittaavat siihen, että syväoppimiseen perustuvat työkalut tunnistavat lncRNA:t parhaiten

PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets

Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify them in transcriptomic data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed for prediction of lncRNAs based on the GENCODE, NONCODE and CANTATAdb databases. The prediction accuracy of these tools often drops when tested on transcriptomic datasets. This leads to higher false positive results and inaccuracy in the function annotation process. In this study, we present a novel tool, PLIT, for the identification of lncRNAs in plants RNA-seq datasets. PLIT implements a feature selection method based on L1 regularization and iterative Random Forests (iRF) classification for selection of optimal features. Based on sequence and codon-bias features, it classifies the RNA-seq derived FASTA sequences into coding or long non-coding transcripts. Using L1 regularization, 31 optimal features were obtained based on lncRNA and protein-coding transcripts from 8 plant species. The performance of the tool was evaluated on 7 plant RNA-seq datasets using 10-fold cross-validation. The analysis exhibited superior accuracy when evaluated against currently available state-of-the-art CPC tools

Computational Methods for Comparative Non-coding RNA Analysis: from Secondary Structures to Tertiary Structures

Unlike message RNAs (mRNAs) whose information is encoded in the primary sequences, the cellular roles of non-coding RNAs (ncRNAs) originate from the structures. Therefore studying the structural conservation in ncRNAs is important to yield an in-depth understanding of their functionalities. In the past years, many computational methods have been proposed to analyze the common structural patterns in ncRNAs using comparative methods. However, the RNA structural comparison is not a trivial task, and the existing approaches still have numerous issues in efficiency and accuracy. In this dissertation, we will introduce a suite of novel computational tools that extend the classic models for ncRNA secondary and tertiary structure comparisons. For RNA secondary structure analysis, we first developed a computational tool, named PhyloRNAalifold, to integrate the phylogenetic information into the consensus structural folding. The underlying idea of this algorithm is that the importance of a co-varying mutation should be determined by its position on the phylogenetic tree. By assigning high scores to the critical covariances, the prediction of RNA secondary structure can be more accurate. Besides structure prediction, we also developed a computational tool, named ProbeAlign, to improve the efficiency of genome-wide ncRNA screening by using high-throughput RNA structural probing data. It treats the chemical reactivities embedded in the probing information as pairing attributes of the searching targets. This approach can avoid the time-consuming base pair matching in the secondary structure alignment. The application of ProbeAlign to the FragSeq datasets shows its capability of genome-wide ncRNAs analysis. For RNA tertiary structure analysis, we first developed a computational tool, named STAR3D, to find the global conservation in RNA 3D structures. STAR3D aims at finding the consensus of stacks by using 2D topology and 3D geometry together. Then, the loop regions can be ordered and aligned according to their relative positions in the consensus. This stack-guided alignment method adopts the divide-and-conquer strategy into RNA 3D structural alignment, which has improved its efficiency dramatically. Furthermore, we also have clustered all loop regions in non-redundant RNA 3D structures to de novo detect plausible RNA structural motifs. The computational pipeline, named RNAMSC, was extended to handle large-scale PDB datasets, and solid downstream analysis was performed to ensure the clustering results are valid and easily to be applied to further research. The final results contain many interesting variations of known motifs, such as GNAA tetraloop, kink-turn, sarcin-ricin and t-loops. We also discovered novel functional motifs that conserved in a wide range of ncRNAs, including ribosomal RNA, sgRNA, SRP RNA, GlmS riboswitch and twister ribozyme

ReAlignerV: Web-based genomic alignment tool with high specificity and robustness estimated by species-specific insertion sequences

<p>Abstract</p> <p>Background</p> <p>Detecting conserved noncoding sequences (CNSs) across species highlights the functional elements. Alignment procedures combined with computational prediction of transcription factor binding sites (TFBSs) can narrow down key regulatory elements. Repeat masking processes are often performed before alignment to mask insertion sequences such as transposable elements (TEs). However, recently such TEs have been reported to influence the gene regulatory network evolution. Therefore, an alignment approach that is robust to TE insertions is meaningful for finding novel conserved TFBSs in TEs.</p> <p>Results</p> <p>We constructed a web server 'ReAlignerV' for complex alignment of genomic sequences. ReAlignerV returns ladder-like schematic alignments that integrate predicted TFBSs and the location of TEs. It also provides pair-wise alignments in which the predicted TFBS sites and their names are shown alongside each sequence. Furthermore, we evaluated false positive aligned sites by focusing on the species-specific TEs (SSTEs), and found that ReAlignerV has a higher specificity and robustness to insertions for sequences having more than 20% TE content, compared to LAGAN, AVID, MAVID and BLASTZ.</p> <p>Conclusion</p> <p>ReAlignerV can be applied successfully to TE-insertion-rich sequences without prior repeat masking, and this increases the chances of finding regulatory sequences hidden in TEs, which are important sources of the regulatory network evolution. ReAlignerV can be accessed through and downloaded from <url>http://genet.med.kagawa-u.ac.jp/</url>.</p

Doctor of Philosophy

dissertationAdvances in technology have produced efficient and powerful scientific instruments for measuring biological phenomena. In particular, modern microscopes and nextgeneration sequencing machines produce data at such a rate that manual analysis is no longer practical or feasible for meaningful scientific inquiries. Thus, there is a great need for computational strategies to organize and analyze huge amounts of data produced by biological experiments. My work presents computational strategies and software solutions for application in image analysis, human variant prioritization, and metagenomics. The information content of images can be leveraged to answer an extremely broad spectrum of questions ranging from inquiries about basic biological processes to highly specific, application-driven inquiries like the efficacy of a pharmaceutical drug. Modern microscopes can produce images at a rate at which rigorous manual analysis is impossible. I have created software pipelines that automate image analysis in two specific applications domains. In addition, I discuss general image analysis strategies that can be applied to a wide variety of problems. There are tens of millions of known human genetic variants. Prioritizing human variants based on how likely they are to cause disease is of huge importance because of the potential impact on human health. Current variant prioritization methods are limited by their scope, efficiency, and accuracy. I present a variant prioritization method, the VAAST variant prioritizer, which is superior in its scope, efficiency, and accuracy to existing variant prioritization methods. The rise of next-generation sequencing enables huge quantities of sequence to be generated in a short period of time. No field of study has been affected by rapid sequencing more than metagenomics. Metagenomics, the genomic analysis of a population v of microorganisms, has important implications for pathogen detection because metagenomics enables the culture-free detection of microorganisms. I have created Taxonomer, a comprehensive metagenomics pipeline that enables the real-time analysis of read datasets derived from environmental samples

A new computational framework for the classification and function prediction of long non-coding RNAs

Long non-coding RNAs (lncRNAs) are known to play a significant role in several biological processes. These RNAs possess sequence length greater than 200 base pairs (bp), and so are often misclassified as protein-coding genes. Most Coding Potential Computation (CPC) tools fail to accurately identify, classify and predict the biological functions of lncRNAs in plant genomes, due to previous research being limited to mammalian genomes. In this thesis, an investigation and extraction of various sequence and codon-bias features for identification of lncRNA sequences has been carried out, to develop a new CPC Framework. For identification of essential features, the framework implements regularisation-based selection. A novel classification algorithm is implemented, which removes the dependency on experimental datasets and provides a coordinate-based solution for sub-classification of lncRNAs. For imputing the lncRNA functions, lncRNA-protein interactions have been first determined through co-expression of genes which were re-analysed by a sequence similaritybased approach for identification of novel interactions and prediction of lncRNA functions in the genome. This integrates a D3-based application for visualisation of lncRNA sequences and their associated functions in the genome. Standard evaluation metrics such as accuracy, sensitivity, and specificity have been used for benchmarking the performance of the framework against leading CPC tools. Case study analyses were conducted with plant RNA-seq datasets for evaluating the effectiveness of the framework using a cross-validation approach. The tests show the framework can provide significant improvements on existing CPC models for plant genomes: 20-40% greater accuracy. Function prediction analysis demonstrates results are consistent with the experimentally-published findings

Detecting the limits of regulatory element conservation and divergence estimation using pairwise and multiple alignments

BACKGROUND: Molecular evolutionary studies of noncoding sequences rely on multiple alignments. Yet how multiple alignment accuracy varies across sequence types, tree topologies, divergences and tools, and further how this variation impacts specific inferences, remains unclear. RESULTS: Here we develop a molecular evolution simulation platform, CisEvolver, with models of background noncoding and transcription factor binding site evolution, and use simulated alignments to systematically examine multiple alignment accuracy and its impact on two key molecular evolutionary inferences: transcription factor binding site conservation and divergence estimation. We find that the accuracy of multiple alignments is determined almost exclusively by the pairwise divergence distance of the two most diverged species and that additional species have a negligible influence on alignment accuracy. Conserved transcription factor binding sites align better than surrounding noncoding DNA yet are often found to be misaligned at relatively short divergence distances, such that studies of binding site gain and loss could easily be confounded by alignment error. Divergence estimates from multiple alignments tend to be overestimated at short divergence distances but reach a tool specific divergence at which they cease to increase, leading to underestimation at long divergences. Our most striking finding was that overall alignment accuracy, binding site alignment accuracy and divergence estimation accuracy vary greatly across branches in a tree and are most accurate for terminal branches connecting sister taxa and least accurate for internal branches connecting sub-alignments. CONCLUSION: Our results suggest that variation in alignment accuracy can lead to errors in molecular evolutionary inferences that could be construed as biological variation. These findings have implications for which species to choose for analyses, what kind of errors would be expected for a given set of species and how multiple alignment tools and phylogenetic inference methods might be improved to minimize or control for alignment errors

BRASERO: A Resource for Benchmarking RNA Secondary Structure Comparison Algorithms

The pairwise comparison of RNA secondary structures is a fundamental problem, with direct application in mining databases for annotating putative noncoding RNA candidates in newly sequenced genomes. An increasing number of software tools are available for comparing RNA secondary structures, based on different models (such as ordered trees or forests, arc annotated sequences, and multilevel trees) and computational principles (edit distance, alignment). We describe here the website BRASERO that offers tools for evaluating such software tools on real and synthetic datasets