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PubMed2006

Partial least squares: a versatile tool for the analysis of high-dimensional genomic data.

Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as, e.g. tumor classification from transcriptome data, identification of relevant genes, survival analysis and modeling of gene networks and transcription factor activities.

Animals↗

PubMed2026

abCRISPR: deep learning-based design of abasic gRNA sequences for specific CRISPR-Cas9 genome editing.

SUMMARY: CRISPR-Cas9 has become a widely used tool for genome editing. However, its off-target cleavage caused by partial sequence matches with guide RNAs (gRNAs) remains a critical limitation. Recently, abasic gRNAs (ØXØ) have been developed to enhance target specificity, but their effects vary depending on the positional sequence context. Here, we present abCRISPR, a deep neural network (DNN) framework for the rational design of ØXØ sequences with minimized off-target activity. abCRISPR leverages informative few-shot training with paired datasets of abasic and unmodified gRNAs, using high-quality random mismatch target libraries, exhaustively sequenced for mismatched off-target substrates (n = 97583) in in vitro CRISPR-Cas9 cleavage experiments. Predicted off-target activities for both abasic and unmodified gRNAs showed strong correlation with experimental data (r ≥ 0.95, 10-fold cross-validation). Notably, these comprehensive training sets provide robust ground-truth negatives, enabling accurate and sensitive prediction of off-targets. For unmodified gRNAs, abCRISPR (AUC = 0.98) was validated to outperform existing deep learning-based methods (AUC = 0.45-0.68). When applied to the human genome, abCRISPR generated ØXØ sequences, covering 58 875 004 potent CRISPR-targetable sites with improved target specificity. Together, this work provides a comprehensive bioinformatics resource for safe and precise CRISPR-Cas9 genome editing. AVAILABILITY AND IMPLEMENTATION: The source code for abCRISPR and training data are available at https://doi.org/10.5281/zenodo.20398246. abCRISPR results for the human genome are available at http://clip.korea.ac.kr/abCRISPR/.

Deep Learning↗

PubMed2006

Quadfinder: server for identification and analysis of quadruplex-forming motifs in nucleotide sequences.

G-quadruplex secondary structures, which play a structural role in repetitive DNA such as telomeres, may also play a functional role at other genomic locations as targetable regulatory elements which control gene expression. The recent interest in application of quadruplexes in biological systems prompted us to develop a tool for the identification and analysis of quadruplex-forming nucleotide sequences especially in the RNA. Here we present Quadfinder, an online server for prediction and bioinformatics of uni-molecular quadruplex-forming nucleotide sequences. The server is designed to be user-friendly and needs minimal intervention by the user, while providing flexibility of defining the variants of the motif. The server is freely available at URL http://miracle.igib.res.in/quadfinder/.

DNA↗

PubMed2025

CCRR: a user-friendly platform for analyzing complex chromosomal rearrangements in tumors.

SUMMARY: Complex chromosomal rearrangements in tumors involve intricate genomic alterations that significantly affect gene function and contribute to cancer development. Identifying these events is crucial for cancer research but is often challenging due to the complexity and limitations of existing tools. We developed the Complex Chromosomal Rearrangements Resolver (CCRR), a comprehensive, reproducible, and user-friendly platform for analyzing complex rearrangements in tumors. CCRR integrates multiple SV and CNV detection tools within a Docker container environment, simplifying installation and configuration. It can be easily deployed, automating the execution and merging of results, providing high-confidence consensus SV and CNV calls, allowing researchers to efficiently analyze complex chromosomal rearrangements in tumors without extensive bioinformatics expertise. CCRR also includes a web server for one-click analysis and customized visualization. AVAILABILITY AND IMPLEMENTATION: The CCRR platform is freely available at https://www.ccrr.life. Source code and executables can be accessed at https://github.com/laslk/CCRR. An archived version is available at Zenodo: https://doi.org/10.5281/zenodo.15386513.

Software↗

PubMed2026

GBSC: graph-based sequence clustering method for similar short tandem repeats in protein sequences.

MOTIVATION: Short tandem repeats (STRs) are abundant in protein sequences and play important role in determining their structures and functions. Strikingly, the unusual compositional characteristics of tandem repeats break classical sequence analysis tools. RESULTS: Here, we establish the first algorithm to effectively identify and cluster STRs: Graph-Based Sequence Clustering (GBSC) features linear time complexity, and clusters protein sequence fragments based on their STRs, while allowing for insertions and mutations and supporting the analysis of imperfect or cryptic repeats. Due to its computational efficacy, our algorithm can be used to systematically scan for patterns in large datasets. We compare our method both to state-of-the-art methods for identifying STRs in proteins and alternative clustering approaches. Unlike existing STR analysis methods, GBSC clusters repeat patterns rather than raw sequences, operating at the level of structural repeat identity, while tolerating biological variations and preventing erroneous merging of structurally and functionally distinct motifs. Whereas functional annotation is typically only available at the protein level, the functions of individual STRs and sequences of adjacent STRs remain largely unknown. On a challenging use case we here demonstrate and discuss how our method can be used to associate previously unannotated repetitive protein fragments with similar ones, allowing the transfer of annotation by similarity. For the first time, GBSC offers a tool that systematically extends this fundamental bioinformatics principle to low-complexity regions across large datasets. AVAILABILITY AND IMPLEMENTATION: GBSC is available at GitHub https://github.com/patryk-jarnot/GBSC and https://doi.org/10.5281/zenodo.18965247. The data and scripts to reproduce the analysis are available at https://doi.org/10.5281/zenodo.16906653.

Microsatellite Repeats↗

PubMed2004

A simple and fast secondary structure prediction method using hidden neural networks.

MOTIVATION: In this paper, we present a secondary structure prediction method YASPIN that unlike the current state-of-the-art methods utilizes a single neural network for predicting the secondary structure elements in a 7-state local structure scheme and then optimizes the output using a hidden Markov model, which results in providing more information for the prediction. RESULTS: YASPIN was compared with the current top-performing secondary structure prediction methods, such as PHDpsi, PROFsec, SSPro2, JNET and PSIPRED. The overall prediction accuracy on the independent EVA5 sequence set is comparable with that of the top performers, according to the Q3, SOV and Matthew's correlations accuracy measures. YASPIN shows the highest accuracy in terms of Q3 and SOV scores for strand prediction. AVAILABILITY: YASPIN is available on-line at the Centre for Integrative Bioinformatics website (http://ibivu.cs.vu.nl/programs/yaspinwww/) at the Vrije University in Amsterdam and will soon be mirrored on the Mathematical Biology website (http://www.mathbio.nimr.mrc.ac.uk) at the NIMR in London. CONTACT: kxlin@nimr.mrc.ac.uk

Algorithms↗

PubMed2003

Proposed classification of cells in the Foundational Model of Anatomy.

A logical and principled representation of cell types and their component parts could serve as a framework for correlating the various ontologies that are emerging in bioinformatics with a focus on cells and subcellular biological entities. In order to address this need we have extended the Foundational Model of Anatomy (FMA)1,2 from macroscopic to cellular and subcellular anatomical entities. The poster will provide a live demonstration of this implementation.

Anatomy↗

PubMed2006

Enrichment analysis in high-throughput genomics - accounting for dependency in the NULL.

Translating the overwhelming amount of data generated in high-throughput genomics experiments into biologically meaningful evidence, which may for example point to a series of biomarkers or hint at a relevant pathway, is a matter of great interest in bioinformatics these days. Genes showing similar experimental profiles, it is hypothesized, share biological mechanisms that if understood could provide clues to the molecular processes leading to pathological events. It is the topic of further study to learn if or how a priori information about the known genes may serve to explain coexpression. One popular method of knowledge discovery in high-throughput genomics experiments, enrichment analysis (EA), seeks to infer if an interesting collection of genes is 'enriched' for a Consortium particular set of a priori Gene Ontology Consortium (GO) classes. For the purposes of statistical testing, the conventional methods offered in EA software implicitly assume independence between the GO classes. Genes may be annotated for more than one biological classification, and therefore the resulting test statistics of enrichment between GO classes can be highly dependent if the overlapping gene sets are relatively large. There is a need to formally determine if conventional EA results are robust to the independence assumption. We derive the exact null distribution for testing enrichment of GO classes by relaxing the independence assumption using well-known statistical theory. In applications with publicly available data sets, our test results are similar to the conventional approach which assumes independence. We argue that the independence assumption is not detrimental.

Data Interpretation, Statistical↗

PubMed2002

Deriving folds of macromolecular complexes through electron cryomicroscopy and bioinformatics approaches.

Intermediate-resolution (7-9A) structures of large macromolecular complexes can be obtained by electron cryomicroscopy. This structural information, combined with bioinformatics data for the individual protein components or domains, can lead to a fold model for the entire complex. Such approaches have been demonstrated with the 6.8 A structure of the rice dwarf virus to derive models for the major capsid shell proteins.

Amino Acid Sequence↗

PubMed2005

Bioinformatic approaches to assigning protein function from novel sequence data.

The current pace of functional genomic initiatives and genome sequencing projects has provided researchers with a bewildering array of sequence and biological data to analyze. The disease system-driven approach to identifying key genes frequently identifies nucleotide and protein sequences for which the gene and protein function are not known in sufficient detail to allow informed follow-up. Using a range of bioinformatic tools and sequence-based clues, most of unassigned sequences can now be annotated. This chapter takes as an example an unannotated expressed sequence tag, describing how to identify its related gene, and how to annotate the encoded protein using sequence, profile, and structure-based annotation methodologies.

Computational Biology↗

PubMed2006

Biotool2Web: creating simple Web interfaces for bioinformatics applications.

UNLABELLED: Currently there are many bioinformatics applications being developed, but there is no easy way to publish them on the World Wide Web. We have developed a Perl script, called Biotool2Web, which makes the task of creating web interfaces for simple ('home-made') bioinformatics applications quick and easy. Biotool2Web uses an XML document containing the parameters to run the tool on the Web, and generates the corresponding HTML and common gateway interface (CGI) files ready to be published on a web server. AVAILABILITY: This tool is available for download at URL http://www.uni-muenster.de/Bioinformatics/services/biotool2web/ CONTACT: Georg Fuellen (fuellen@alum.mit.edu).

Computational Biology↗

PubMed1999

Integrating Application Programs for Bioinformatics Using a Web Browser.

We have constructed a general framework for integrating application programs with control through a local Web browser. This method is based on a simple inter-process message function from an external process to application programs. Commands to a target program are prepared in a script file, which is parsed by a message dispatcher program. When it is used as a helper application to a Web browser, these messages will be sent from the browser by clicking a hyper-link in a Web document. Our framework also supports pluggable extension-modules for application programs by means of dynamic linking. A prototype system is implemented on our molecular structure-viewer program, MOSBY. It successfully featured a function to load an extension-module required for the docking study of molecular fragments from a Web page. Our simple framework facilitates the concise configuration of Web softwares without complicated knowledge on network computation and security issues. It is also applicable for a wide range of network computations processing private data using a Web browser.

Journal Article↗

PubMed2002

Using CAVE technology for functional genomics studies.

We have established the first Java 3D-enabled CAVE (CAVE automated virtual environment). The Java application programming interface allows the complete separation of the program development from the program execution, opening new application domains for the CAVE technology. Programs can be developed on any Java-enabled computer platform, including Windows, Macintosh, and Linux workstations, and executed in the CAVE without modification. The introduction of Java, one of the major programming environments for bioinformatics, into the CAVE environment allows the rapid development applications for genome research, especially for the analysis of the spatial and temporal data that are being produced by functional genomics experiments. The CAVE technology will play a major role in the modeling of biological systems that is necessary to understand how these systems are organized and how they function.

Automation↗

PubMed2001

A new approach to sequence comparison: normalized sequence alignment.

The Smith-Waterman algorithm for local sequence alignment is one of the most important techniques in computational molecular biology. This ingenious dynamic programming approach was designed to reveal the highly conserved fragments by discarding poorly conserved initial and terminal segments. However, the existing notion of local similarity has a serious flaw: it does not discard poorly conserved intermediate segments. The Smith-Waterman algorithm finds the local alignment with maximal score but it is unable to find local alignment with maximum degree of similarity (e.g. maximal percent of matches). Moreover, there is still no efficient algorithm that answers the following natural question: do two sequences share a (sufficiently long) fragment with more than 70% of similarity? As a result, the local alignment sometimes produces a mosaic of well-conserved fragments artificially connected by poorly-conserved or even unrelated fragments. This may lead to problems in comparison of long genomic sequences and comparative gene prediction as recently pointed out by Zhang et al. (Bioinformatics, 15, 1012-1019, 1999). In this paper we propose a new sequence comparison algorithm (normalized local alignment ) that reports the regions with maximum degree of similarity. The algorithm is based on fractional programming and its running time is O(n2log n). In practice, normalized local alignment is only 3-5 times slower than the standard Smith-Waterman algorithm.

Algorithms↗

PubMed2006

Protein expression profiling of postmortem brain in schizophrenia.

Surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) enables the sensitive, high-throughput protein profiling of complex biological mixtures. In combination with bioinformatics, this technology has the potential to identify combinations of spectral peaks that can differentiate individuals with a particular disease from normal controls. SELDI-TOF-MS was used to screen postmortem tissue derived from the dorsolateral prefrontal cortex of individuals with schizophrenia (n = 34) and matched controls (n = 35), obtained from the Stanley Foundation Neuropathology Consortium. Tissue samples were homogenized in urea buffer, applied to four different chip arrays which possess different chromatographic surfaces, and analyzed using the Ciphergen ProteinChip Biomarkers System (Model PBS II). Protein expression profiles of the schizophrenia and control groups were compared and analyzed using the Ciphergen Express (CE) and Biomarker Patterns Software (BPS) package. We detected several protein peaks whose intensities differed between the schizophrenia and control groups to a highly significant degree. A combination of these peaks was capable of distinguishing between schizophrenia and controls with a sensitivity and specificity of about 70%. The classification model that distinguished schizophrenia from controls was complex, suggesting that the biochemical abnormalities underlying schizophrenia are heterogeneous. Our results suggest that SELDI-TOF-MS has the potential for distinguishing individuals with schizophrenia from normal controls and may eventually lead to a better understanding of the classification, diagnosis and pathogenesis of this disorder.

Adult↗

PubMed2005

Improving interoperability between microbial information and sequence databases.

BACKGROUND: Biological resources are essential tools for biomedical research. Their availability is promoted through on-line catalogues. Common Access to Biological Resources and Information (CABRI) is a service for distribution of biological resources and related data collected by 28 European culture collections. Linking this information to bioinformatics databanks can make the collections' holdings more visible after a search in molecular biology databanks and vice-versa. Identification of links to sequence databases can be useful, but annotation and indexing problems, together with compilation errors, immediately arise. In this paper, we present our efforts for the identification of cross-references between CABRI catalogues and the EMBL Data Library and related results. RESULTS: An SRS site with both EMBL and CABRI catalogues has been set up. Ad-hoc changes in indexing scripts allowed to achieve homogeneous index keys and SRS link features have been used to identify links between databases. After manual checking and comparison with an alternative procedure, about 67,500 valid cross-references were identified, added to the EMBL Data Library and are now distributed with it. HTML links can be established from EMBL to CABRI network service. Procedures can be executed whenever needed. CONCLUSION: Links between EMBL and CABRI catalogues constitute an improved access to micro-organisms of certified quality and can produce positive effects on biomedical research. Further links between CABRI catalogues and other bioinformatics databases can now easily be defined by using these cross-references. Linking genetic information onto natural resources information may stand model for the integration of other databases containing empirical data on these materials.

Base Sequence↗

PubMed2006

TF Target Mapper: a BLAST search tool for the identification of Transcription Factor target genes.

BACKGROUND: In the current era of high throughput genomics a major challenge is the genome-wide identification of target genes for specific transcription factors. Chromatin immunoprecipitation (ChIP) allows the isolation of in vivo binding sites of transcription factors and provides a powerful tool for examining gene regulation. Crosslinked chromatin is immunoprecipitated with antibodies against specific transcription factors, thus enriching for sequences bound in vivo by these factors in the immunoprecipitated DNA. Cloning and sequencing the immunoprecipitated sequences allows identification of transcription factor target genes. Routinely, thousands of such sequenced clones are used in BLAST searches to map their exact location in the genome and the genes located in the vicinity. These genes represent potential targets of the transcription factor of interest. Such bioinformatics analysis is very laborious if performed manually and for this reason there is a need for developing bioinformatic tools to automate and facilitate it. RESULTS: In order to facilitate this analysis we generated TF Target Mapper (Transcription Factor Target Mapper). TF Target Mapper is a BLAST search tool allowing rapid extraction of annotated information on genes around each hit. It combines sequence cleaning/filtering, pattern searching and BLAST searches with extraction of information on genes located around each BLAST hit and comparisons of the output list of genes or gene ontology IDs with user-implemented lists. We successfully applied and tested TF Target Mapper to analyse sequences bound in vivo by the transcription factor GATA-1. We show that TF Target Mapper efficiently extracted information on genes around ChIPed sequences, thus identifying known (e.g. alpha-globin and zeta-globin) and potentially novel GATA-1 gene targets. CONCLUSION: TF Target Mapper is a very efficient BLAST search tool that allows the rapid extraction of annotated information on the genes around each hit. It can contribute to the comprehensive bioinformatic transcriptome/regulome analysis, by providing insight into the mechanisms of action of specific transcription factors, thus helping to elucidate the pathways these factors regulate.

Amino Acid Sequence↗

PubMed2004

Finding potential ligands for PDZ domains by tailfit, a JAVA program.

OBJECTIVE: To deduce all potential ligands undiscovered experimentally by searching all the proteins containing same C-termini, which can bind a certain PDZ domain. METHODS: We developed a JAVA program for searching short exact sequence matches at C-terminus. According to the known C-termini, which PDZ domains recognized experimentally, Swissprot database has been searched by this program for all potential ligands. RESULTS: Some PDZ domains may have more potential ligand proteins, which are undiscovered yet experimentally. These bioinformatic results also provide clues for studying functions of hypothetical proteins and PDZ domains' protein interactions in many different organisms. CONCLUSION: The results may provide useful clues for discovering potential functions of hypothetical proteins and new functions of known proteins.

Amino Acid Sequence↗