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PubMed1988

Biological applications of the SAS system: an overview.

The SAS system provides biologists with a flexible, easy to use software package for data analysis. Through a combination of data management tools, a wide variety of pre-programmed procedures for sorting, graphing, and statistical analysis and a sophisticated programming language, SAS software can perform all analytical needs for most problems. The recent availability of SAS software on mainframes other than IBM, and more recently on the microcomputer, means that most scientists can have access to the software. In this review we discuss the structure of the SAS language and demonstrate its power in the analysis of biological problems. Although to a lesser extent now than originally, the SAS system is statistically oriented and a working knowledge of statistics is recommended before using its statistical capabilities. However, all biologists will find its data management and summarization capabilities very useful.

Biology↗

PubMed2025

Realfreq: real-time base modification analysis for nanopore sequencing.

SUMMARY: Nanopore sequencers allow sequencing data to be accessed in real-time. This allows live analysis to be performed, while the sequencing is running, reducing the turnaround time of the results. We introduce realfreq, a framework for obtaining real-time base modification frequencies while a nanopore sequencer is in operation. Realfreq calculates and allows access to the real-time base modification frequency results while the sequencer is running. We demonstrate that the data analysis rate with realfreq on a laptop computer can keep up with the output data rate of a nanopore MinION sequencer, while a desktop computer can keep up with a single PromethION 2 solo flowcell. AVAILABILITY AND IMPLEMENTATION: Realfreq is a free and open-source application implemented in C programming language and shell scripts. The source code and the documentation for realfreq can be found at https://github.com/imsuneth/realfreq. The version used for the manuscript is also available at https://doi.org/10.5281/zenodo.15128668.

Nanopore Sequencing↗

PubMed2026

A novel method for across-chromosome phasing without relative data.

MOTIVATION: Across-chromosome phasing identifies which haplotypes of different chromosomes come from the same parent. This differs from within-chromosome phasing, which uses linkage disequilibrium patterns to determine which alleles were co-inherited within each chromosome but does not match haplotypes across different chromosomes. While across-chromosome phasing can be conducted using genotypes from parents or close relatives, current methods perform poorly for samples of unrelated individuals. Here, we introduce a novel approach for across-chromosome phasing that employs a window-based SNP-similarity metric, eliminating the need for data from close relatives or detection of identical-by-descent haplotypes. RESULTS: Using UK Biobank offspring with both parents genotyped as a gold standard, we evaluated the performance of our method by phasing the offspring without using parental data. In genomic data with no within-chromosome phase errors, our algorithm achieved a mean across-chromosome phasing accuracy of 95%, with 53% of individuals phased perfectly. When data was pre-phased computationally using a standard within-chromosome phasing algorithm, mean accuracy for across-chromosome phasing dropped to 83.1%. Thus, our method is limited primarily by the accuracy of within-chromosome phasing accuracy and can approach near-perfect across-chromosome phasing accuracy as within-chromosome phasing accuracy improves. AVAILABILITY AND IMPLEMENTATION: The implementation was executed within a multi-node computational environment of University of Colorado Boulder Research Computing (Blanca Cluster: https://www.colorado.edu/rc/resources/blanca), employing parallelization techniques in the C programming language. The source code has been made publicly accessible online at https://github.com/emmanuelsapin/AcrossChromosomesPhasing, thereby facilitating reproducibility of the results for researchers with authorized access to the UK Biobank dataset.

Algorithms↗

PubMed2026

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

MOTIVATION: CRISPR-Cas9 genome-editing efficiency is strongly influenced by the sequence composition and positional context of single-guide RNAs (sgRNAs). Although numerous deep learning-based models have been developed to predict Cas9 efficiency from sgRNA sequences, most operate as black boxes, offering limited insight into the sequence determinants underlying Cas9 activity. In addition, previous studies often overlook how the positional context of sequence motifs within sgRNAs influences their effects on Cas9 binding or cleavage. RESULTS: We introduce DeepCC9, an interpretable machine learning framework that combines explicit sequence feature extraction with a residual block-based deep architecture to improve interpretability and identify composition- and position-based motifs governing Cas9 genome-editing efficiency. We applied this method to multiple Cas9 variant datasets, achieving superior predictive performance compared with existing methods while enabling direct interpretation of sequence motifs and their positional effects. Our analysis uncovered 74 sequence motifs enriched or depleted at specific positions within sgRNAs and strongly associated with Cas9 efficiency, providing mechanistic insight into sequence features that influence guide performance. Together, these results establish DeepCC9 as a generalizable and interpretable framework for modeling sequence-function relationships and advancing the understanding of the sequence determinants underlying CRISPR-Cas9 genome editing. AVAILABILITY AND IMPLEMENTATION: The authors have implemented their algorithm in the Python programming language (version 3.X), which is accessible using (https://zenodo.org/records/20073890).

Deep Learning↗

PubMed2026

SNPannotator: automated functional annotation of genetic variants and linked proxies.

SUMMARY: Genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits and diseases. However, explaining the mechanisms underlying phenotypic variation remains challenging. Here, we introduce SNPannotator, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings. Our pipeline implements a multi-step process that identifies proxy variants in high linkage disequilibrium (LD) with associated lead variants, then queries comprehensive resources (including Ensembl, the GTEx Portal, the eQTL Catalog, and STRING DB) for genomic position, deleteriousness, regulatory annotations, clinical significance, trait associations, expression (eQTLs) and splicing quantitative trait loci (sQTLs), and functional enrichment analyses and compiles the results into user-friendly reports. This package is implemented in the R programming language and includes auxiliary functions for variant lookup and LD exploration. SNPannotator provides a practical framework for efficiently deriving biologically meaningful insights from GWAS data and for assisting researchers in prioritizing candidate variants for functional validation. AVAILABILITY AND IMPLEMENTATION: The SNPannotator package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/SNPannotator. The development version and tutorial is available on GitHub (https://github.com/omicslaboratory/SNPannotator). The online version of the package is available at https://omicslab.org/snpannotator.

Software↗

PubMed2003

Reconstructing the temporal ordering of biological samples using microarray data.

MOTIVATION: Accurate time series for biological processes are difficult to estimate due to problems of synchronization, temporal sampling and rate heterogeneity. Methods are needed that can utilize multi-dimensional data, such as those resulting from DNA microarray experiments, in order to reconstruct time series from unordered or poorly ordered sets of observations. RESULTS: We present a set of algorithms for estimating temporal orderings from unordered sets of sample elements. The techniques we describe are based on modifications of a minimum-spanning tree calculated from a weighted, undirected graph. We demonstrate the efficacy of our approach by applying these techniques to an artificial data set as well as several gene expression data sets derived from DNA microarray experiments. In addition to estimating orderings, the techniques we describe also provide useful heuristics for assessing relevant properties of sample datasets such as noise and sampling intensity, and we show how a data structure called a PQ-tree can be used to represent uncertainty in a reconstructed ordering. AVAILABILITY: Academic implementations of the ordering algorithms are available as source code (in the programming language Python) on our web site, along with documentation on their use. The artificial 'jelly roll' data set upon which the algorithm was tested is also available from this web site. The publicly available gene expression data may be found at http://genome-www.stanford.edu/cellcycle/ and http://caulobacter.stanford.edu/CellCycle/.

Algorithms↗

PubMed2004

Fuzzy J-Means and VNS methods for clustering genes from microarray data.

MOTIVATION: In the interpretation of gene expression data from a group of microarray experiments that include samples from either different patients or conditions, special consideration must be given to the pleiotropic and epistatic roles of genes, as observed in the variation of gene coexpression patterns. Crisp clustering methods assign each gene to one cluster, thereby omitting information about the multiple roles of genes. RESULTS: Here, we present the application of a local search heuristic, Fuzzy J-Means, embedded into the variable neighborhood search metaheuristic for the clustering of microarray gene expression data. We show that for all the datasets studied this algorithm outperforms the standard Fuzzy C-Means heuristic. Different methods for the utilization of cluster membership information in determining gene coregulation are presented. The clustering and data analyses were performed on simulated datasets as well as experimental cDNA microarray data for breast cancer and human blood from the Stanford Microarray Database. AVAILABILITY: The source code of the clustering software (C programming language) is freely available from Nabil.Belacel@nrc-cnrc.gc.ca

Algorithms↗

PubMed2004

Regulatory motif finding by logic regression.

MOTIVATION: Multiple transcription factors coordinately control transcriptional regulation of genes in eukaryotes. Although many computational methods consider the identification of individual transcription factor binding sites (TFBSs), very few focus on the interactions between these sites. We consider finding TFBSs and their context specific interactions using microarray gene expression data. We devise a hybrid approach called LogicMotif composed of a TFBS identification method combined with the new regression methodology logic regression. LogicMotif has two steps: First, potential binding sites are identified from transcription control regions of genes of interest. Various available methods can be used in this step when the genes of interest can be divided into groups such as up-and downregulated. For this step, we also develop a simple univariate regression and extension method MFURE to extract candidate TFBSs from a large number of genes in the availability of microarray gene expression data. MFURE provides an alternative method for this step when partitioning of the genes into disjoint groups is not preferred. This first step aims to identify individual sites within gene groups of interest or sites that are correlated with the gene expression outcome. In the second step, logic regression is used to build a predictive model of outcome of interest (either gene expression or up- and down-regulation) using these potential sites. This 2-fold approach creates a rich diverse set of potential binding sites in the first step and builds regression or classification models in the second step using logic regression that is particularly good at identifying complex interactions. RESULTS: LogicMotif is applied to two publicly available datasets. A genome-wide gene expression data set of Saccharomyces cerevisiae is used for validation. The regression models obtained are interpretable and the biological implications are in agreement with the known resuts. This analysis suggests that LogicMotif provides biologically more reasonable regression models than previous analysis of this dataset with standard linear regression methods. Another dataset of S.cerevisiae illustrates the use of LogicMotif in classification questions by building a model that discriminates between up- and down-regulated genes in iron copper deficiency. LogicMotif identifies an inductive and two repressor motifs in this dataset. The inductive motif matches the binding site of the transcription factor Aft1p that has a key role in regulation of the uptake process. One of the novel repressor sites is highly present in transcription control regions of FeS genes. This site could represent a TFBS for an unknown transcription factor involved in repression of genes encoding FeS proteins in iron deficiency. We establish the robustness of the method to the type of outcome variable used by considering both continuous and binary outcome variables for this dataset. Our results indicate that logic regression used in combination with cluster/group operating binding site identification methods or with our proposed method MFURE is a powerful and flexible alternative to linear regression based motif finding methods. AVAILABILITY: Source code for logic regression is freely available as a package of the R programming language by Ruczinski et al. (2003) and can be downloaded at http://bear.fhcrc.org/~ingor/logic/download/download.html an R package for MFURE is available at http://www.stat.berkeley.edu/~sunduz/software.html

Algorithms↗

PubMed2004

An efficient algorithm for detecting frequent subgraphs in biological networks.

MOTIVATION: With rapidly increasing amount of network and interaction data in molecular biology, the problem of effectively analyzing this data is an important one. Graph theoretic formalisms, commonly used for these analysis tasks, often lead to computationally hard problems due to their relation with subgraph isomorphism. RESULTS: This paper presents an innovative new algorithm for detecting frequently occurring patterns and modules in biological networks. Using an innovative graph simplification technique, which is ideally suited to biological networks, our algorithm renders these problems computationally tractable. Indeed, we show experimentally that our algorithm can extract frequently occurring patterns in metabolic pathways extracted from the KEGG database within seconds. The proposed model and algorithm are applicable to a variety of biological networks either directly or with minor modifications. AVAILABILITY: Implementation of the proposed algorithms in the C programming language is available as open source at http://www.cs.purdue.edu/homes/koyuturk/pathway/

Algorithms↗

PubMed2004

Clann: investigating phylogenetic information through supertree analyses.

UNLABELLED: Clann has been developed in order to provide methods of investigating phylogenetic information through the application of supertrees. AVAILABILITY: Clann has been precompiled for Linux, Apple Macintosh and Windows operating systems and is available from http://bioinf.may.ie/software/clann. Source code is available on request from the authors. SUPPLEMENTARY INFORMATION: Clann has been written in the C programming language. Source code is available on request.

Algorithms↗

PubMed2004

A framework for scientific data modeling and automated software development.

MOTIVATION: The lack of standards for storage and exchange of data is a serious hindrance for the large-scale data deposition, data mining and program interoperability that is becoming increasingly important in bioinformatics. The problem lies not only in defining and maintaining the standards, but also in convincing scientists and application programmers with a wide variety of backgrounds and interests to adhere to them. RESULTS: We present a UML-based programming framework for the modeling of data and the automated production of software to manipulate that data. Our approach allows one to make an abstract description of the structure of the data used in a particular scientific field and then use it to generate fully functional computer code for data access and input/output routines for data storage, together with accompanying documentation. This code can be generated simultaneously for different programming languages from a single model, together with, for example for format descriptions and I/O libraries XML and various relational databases. The framework is entirely general and could be applied in any subject area. We have used this approach to generate a data exchange standard for structural biology and analysis software for macromolecular NMR spectroscopy. AVAILABILITY: The framework is available under the GPL license, the data exchange standard with generated subroutine libraries under the LGPL license. Both may be found at http://www.ccpn.ac.uk; http://sourceforge.net/projects/ccpn CONTACT: ccpn@mole.bio.cam.ac.uk.

Biopolymers↗

PubMed2005

Fast parsers for Entrez Gene.

NCBI completed the transition of its main genome annotation database from Locuslink to Entrez Gene in Spring 2005. However, to this date few parsers exist for the Entrez Gene annotation file. Owing to the widespread use of Locuslink and the popularity of Perl programming language in bioinformatics, a publicly available high performance Entrez Gene parser in Perl is urgently needed. We present four such parsers that were developed using several parsing approaches (Parse::RecDescent, Parse::Yapp, Perl-byacc and Perl 5 regular expressions) and provide the first in-depth comparison of these sophisticated Perl tools. Our fastest parser processes the entire human Entrez Gene annotation file in under 12 min on one Intel Xeon 2.4 GHz CPU and can be of help to the bioinformatics community during and after the transition from Locuslink to Entrez Gene.

Algorithms↗

PubMed2006

GeneRecon--a coalescent based tool for fine-scale association mapping.

UNLABELLED: GeneRecon is a tool for fine-scale association mapping using a coalescence model. GeneRecon takes as input case-control data from phased or unphased SNP and microsatellite genotypes. The posterior distribution of disease locus position is obtained by Metropolis-Hastings sampling in the state space of genealogies. Input format, search strategy and the sampled statistics can be configured through the Guile Scheme programming language embedded in GeneRecon, making GeneRecon highly configurable. AVAILABILITY: The source code for GeneRecon, written in C++ and Scheme, is available under the GNU General Public License (GPL) at http://www.birc.au.dk/~mailund/GeneRecon CONTACT: mailund@birc.au.dk.

Algorithms↗

PubMed1984

Microcomputer programs for back translation of protein to DNA sequences and analysis of ambiguous DNA sequences.

Three computer programs are described which may be used to translate a DNA sequence into a protein sequence, back translate the protein sequence into an ambiguous DNA sequence, and then do pattern searching in the ambiguous sequence. The programs are written in the C programming language, have been compiled to run on a microcomputer under the CP/M 80 operating system, and may be copied in binary format through a modem. They are also to become available for the IBM/PC.

Amino Acid Sequence↗

PubMed1988

Apple Macintosh programs for nucleic and protein sequence analyses.

This paper describes a package of programs for handling and analyzing nucleic acid and protein sequences using the Apple Macintosh microcomputer. There are three important features of these programs: first, because of the now classical Macintosh interface the programs can be easily used by persons with little or no computer experience. Second, it is possible to save all the data, written in an editable scrolling text window or drawn in a graphic window, as files that can be directly used either as word processing documents or as picture documents. Third, sequences can be easily exchanged with any other computer. The package is composed of thirteen programs, written in Pascal programming language.

Amino Acid Sequence↗

PubMed1997

MIPS: a database for protein sequences, homology data and yeast genome information.

The MIPS group (Martinsried Institute for Protein Sequences) at the Max-Planck-Institute for Biochemistry, Martinsried near Munich, Germany, collects, processes and distributes protein sequence data within the framework of the tripartite association of the PIR-International Protein Sequence Database (,). MIPS contributes nearly 50% of the data input to the PIR-International Protein Sequence Database. The database is distributed on CD-ROM together with PATCHX, an exhaustive supplement of unique, unverified protein sequences from external sources compiled by MIPS. Through its WWW server (http://www.mips.biochem.mpg.de/ ) MIPS permits internet access to sequence databases, homology data and to yeast genome information. (i) Sequence similarity results from the FASTA program () are stored in the FASTA database for all proteins from PIR-International and PATCHX. The database is dynamically maintained and permits instant access to FASTA results. (ii) Starting with FASTA database queries, proteins have been classified into families and superfamilies (PROT-FAM). (iii) The HPT (hashed position tree) data structure () developed at MIPS is a new approach for rapid sequence and pattern searching. (iv) MIPS provides access to the sequence and annotation of the complete yeast genome (), the functional classification of yeast genes (FunCat) and its graphical display, the 'Genome Browser' (). A CD-ROM based on the JAVA programming language providing dynamic interactive access to the yeast genome and the related protein sequences has been compiled and is available on request.

Academies and Institutes↗

PubMed1981

Computer programs to assist in high resolution thermal denaturation and circular dichroism studies on nucleic acids.

Computer programs are described that direct the collection, processing, and graphical display of numerical data obtained from high resolution thermal denaturation (1-3) and circular dichroism (4) studies. Besides these specific applications, the programs may also be useful, either directly or as programming models, in other types of spectrophotometric studies employing computers, programming languages, or instruments similar to those described here (see Materials and Methods).

Circular Dichroism↗

PubMed2025

Accurate detection of tandem repeats exposes ubiquitous reuse of biological sequences.

Tandem repetition is one of the major processes underlying genome evolution and phenotypic diversification. While newly formed tandem repeats are often easy to identify, it is more challenging to detect repeat copies as they diverge over evolutionary timescales. Existing programs for finding tandem repeats return markedly different results, and it is unclear which predictions are more correct and how much room remains for improvement. Here, we introduce DetectRepeats, a new method that uses empirical information about structural repeats to improve the accuracy of repeat detection. We show that DetectRepeats advances the state-of-the-art by finding highly divergent repeats with relatively few false positive detections. We apply DetectRepeats to genomes across the tree of life to discover an enrichment of detectable tandem repeats within different genes, genome regions, and taxa. Furthermore, we use phylogenetic reconciliation to determine that some tandem repeats continue to evolve through intra-repeat unit replacement. In this manner, tandem repeats serve as a renewable genetic resource offering a bountiful source of alternative genetic material. Our work unlocks the confident detection of ancient tandem repeats, opening a doorway to future discoveries. DetectRepeats is part of the DECIPHER package for the R programming language and available via Bioconductor.

Tandem Repeat Sequences↗