The "replication crisis" is a methodological problem in which many scientific research findings have been difficult or impossible to replicate. Because the reproducibility of empirical results is an essential aspect of the scientific method, such failures endanger the credibility of theories based on them and possibly significant portions of scientific knowledge. An instance of the replication crisis, analytic replication, pertains to reproducing published results through computational reanalysis of the authors' original data. However, direct replications are costly, time-consuming, and unrewarded in today's publishing standards. We propose that bioinformatics and computational biology students replicate recent discoveries as part of their curriculum. Considering the above, we performed a pilot study in one of the graduate-level courses we developed and taught at our University. The course is entitled Intro to R Programming and is meant for students in our Master's and PhD programs who have little to no programming skills. As the course emphasized real-world data analysis, we thought it would be an appropriate setting to carry out this study. The primary objective was to expose the students to real biological data analysis problems. These include locating and downloading the needed datasets, understanding any underlying conventions and annotations, understanding the analytical methods, and regenerating multiple graphs from their assigned article. The secondary goal was to determine whether the assigned articles contained sufficient information for a graduate-level student to replicate its figures. Overall, the students successfully reproduced 39% of the figures. The main obstacles were the need for more advanced programming skills and the incomplete documentation of the applied methods. Students were engaged, enthusiastic, and focused throughout the semester. We believe that this teaching approach will allow students to make fundamental scientific contributions under appropriate supervision. It will teach them about the scientific process, the importance of reporting standards, and the importance of openness.

MOTIVATION: MicroRNA (miRNA) precursor arms give rise to multiple isoforms simultaneously called 'isomiRs.' IsomiRs from the same arm typically differ by a few nucleotides at either their 5' or 3' termini or both. In humans, the identities and abundances of isomiRs depend on a person's sex and genetic ancestry as well as on tissue type, tissue state and disease type/subtype. Moreover, nearly half of the time the most abundant isomiR differs from the miRNA sequence found in public databases. Accurate mining of isomiRs from deep sequencing data is thus important. RESULTS: We developed isoMiRmap, a fast, standalone, user-friendly mining tool that identifies and quantifies all isomiRs by directly processing short RNA-seq datasets. IsoMiRmap is a portable 'plug-and-play' tool, requires minimal setup, has modest computing and storage requirements, and can process an RNA-seq dataset with 50 million reads in just a few minutes on an average laptop. IsoMiRmap deterministically and exhaustively reports all isomiRs in a given deep sequencing dataset and quantifies them accurately (no double-counting). IsoMiRmap comprehensively reports all miRNA precursor locations from which an isomiR may be transcribed, tags as 'ambiguous' isomiRs whose sequences exist both inside and outside of the space of known miRNA sequences and reports the public identifiers of common single-nucleotide polymorphisms and documented somatic mutations that may be present in an isomiR. IsoMiRmap also identifies isomiRs with 3' non-templated post-transcriptional additions. Compared to similar tools, isoMiRmap is the fastest, reports more bona fide isomiRs, and provides the most comprehensive information related to an isomiR's transcriptional origin. AVAILABILITY AND IMPLEMENTATION: The codes for isoMiRmap are freely available at https://cm.jefferson.edu/isoMiRmap/ and https://github.com/TJU-CMC-Org/isoMiRmap/. IsomiR profiles for the datasets of the 1000 Genomes Project, spanning five population groups, and The Cancer Genome Atlas (TCGA), spanning 33 cancer studies, are also available at https://cm.jefferson.edu/isoMiRmap/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods. RESULTS: To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification. AVAILABILITY AND IMPLEMENTATION: https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: We present 'Threshold-seq,' a new approach for determining thresholds in deep-sequencing datasets of short RNA transcripts. Threshold-seq addresses the critical question of how many reads need to support a short RNA molecule in a given dataset before it can be considered different from 'background.' The proposed scheme is easy to implement and incorporate into existing pipelines. AVAILABILITY AND IMPLEMENTATION: Source code of Threshold-seq is freely available as an R package at: http://cm.jefferson.edu/threshold-seq/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: It has been known that mature transfer RNAs (tRNAs) that are encoded in the nuclear genome give rise to short molecules, collectively known as tRNA fragments or tRFs. Recently, we reported that, in healthy individuals and in patients, tRFs are constitutive, arise from mitochondrial as well as from nuclear tRNAs, and have composition and abundances that depend on a person's sex, population origin and race as well as on tissue, disease and disease subtype. Our findings as well as similar work by other groups highlight the importance of tRFs and presage an increase in the community's interest in elucidating the roles of tRFs in health and disease. RESULTS: We created MINTbase, a web-based framework that serves the dual-purpose of being a content repository for tRFs and a tool for the interactive exploration of these newly discovered molecules. A key feature of MINTbase is that it deterministically and exhaustively enumerates all possible genomic locations where a sequence fragment can be found and indicates which fragments are exclusive to tRNA space, and thus can be considered as tRFs: this is a very important consideration given that the genomes of higher organisms are riddled with partial tRNA sequences and with tRNA-lookalikes whose aberrant transcripts can be mistaken for tRFs. MINTbase is extremely flexible and integrates and presents tRF information from multiple yet interconnected vantage points ('vistas'). Vistas permit the user to interactively personalize the information that is returned and the manner in which it is displayed. MINTbase can report comparative information on how a tRF is distributed across all anticodon/amino acid combinations, provides alignments between a tRNA and multiple tRFs with which the user can interact, provides details on published studies that reported a tRF as expressed, etc. Importantly, we designed MINTbase to contain all possible tRFs that could ever be produced by mature tRNAs: this allows us to report on their genomic distributions, anticodon/amino acid properties, alignments, etc. while giving users the ability to at-will investigate candidate tRF molecules before embarking on focused experimental explorations. Lastly, we also introduce a new labeling scheme that is tRF-sequence-based and allows users to associate a tRF with a universally unique label ('tRF-license plate') that is independent of a genome assembly and does not require any brokering mechanism. AVAILABILITY AND IMPLEMENTATION: MINTbase is freely accessible at http://cm.jefferson.edu/MINTbase/. Dataset submissions to MINTbase can be initiated at http://cm.jefferson.edu/MINTsubmit/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We examine several of the choices that went into the design of tDRmapper, a recently reported tool for identifying transfer RNA (tRNA) fragments in deep sequencing data, evaluate them in the context of currently available knowledge, and discuss their potential impact on the output that the tool generates.

Abstract Summary: MicroRNA (miRNA) target prediction is an important problem. Given an miRNA sequence the task is to determine the identity of the messenger RNAs targeted by it, the locations within them where the interactions happen and the specifics of the formed heteroduplexes. Here, we describe a web-based application, RNA22-GUI, which we have designed and implemented for the interactive exploration and in-context visualization of predictions by RNA22, one of the popular miRNA target prediction algorithms. Central to our design has been the requirement to provide informative and comprehensive visualization that is integrated with interactive search capabilities and permits one to selectively isolate and focus on relevant information that is distilled on-the-fly from a large repository of pre-compiled predictions. RNA22-GUI is currently available for Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. Availability: http://cm.jefferson.edu/rna22v1.0/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

With the advent of next-generation DNA sequencing machines, there is an increasing need for the development of computational tools that can anchor accurately and expediently the millions of generated short DNA sequences (or reads) onto the genomes of target organisms. In this work, we describe 'Q-Pick', a new and efficient method for solving this problem. Q-Pick allows the rapid identification and anchoring of such reads with possible wildcards in large genomic databases, while guaranteeing completeness of results and efficiency of operation. Q-Pick requires very spartan memory and computational resources, and is trivially amenable to SIMD implementation; it can also be easily extended to handle longer reads, e.g. 75-mers or longer. Our experiments indicate that Q-Pick can anchor millions of distinct short reads against both strands of a mammalian genome in seconds, using a single-core computer processor.

Alu and B1 repeats are mobile elements that originated in an initial duplication of the 7SL RNA gene prior to the primate-rodent split about 80 million years ago and currently account for a substantial fraction of the human and mouse genome, respectively. Following the primate-rodent split, Alu and B1 elements spread independently in each of the two genomes in a seemingly random manner, and, according to the prevailing hypothesis, negative selection shaped their final distribution in each genome by forcing the selective loss of certain Alu and B1 copies. In this paper, contrary to the prevailing hypothesis, we present evidence that Alu and B1 elements have been selectively retained in the upstream and intronic regions of genes belonging to specific functional classes. At the same time, we found no evidence for selective loss of these elements in any functional class. A subset of the functional links we discovered corresponds to functions where Alu involvement has actually been experimentally validated, whereas the majority of the functional links we report are novel. Finally, the unexpected finding that Alu and B1 elements show similar biases in their distribution across functional classes, despite having spread independently in their respective genomes, further supports our claim that the extant instances of Alu and B1 elements are the result of positive selection.

MOTIVATION: Motif discovery in sequential data is a problem of great interest and with many applications. However, previous methods have been unable to combine exhaustive search with complex motif representations and are each typically only applicable to a certain class of problems. RESULTS: Here we present a generic motif discovery algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As we show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. Finally, Gemoda's output motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices or any number of other models for any type of sequential data. We demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids sequences, a new solution to the (l,d)-motif problem in DNA sequences and the discovery of conserved protein substructures. AVAILABILITY: Gemoda is freely available at http://web.mit.edu/bamel/gemoda

Systems biology is a field which focuses on the interpretation of large, diverse sets of biological measurements in order to elucidate the complex mechanisms that underly important and (seemingly simple) macroscopic phenotypes. The problem at hand is hierarchical in nature, with the hierarchy spanning many levels. Each of these levels can be thought of as comprising multiple active agents that are diverse in their nature (e.g. genes, proteins, pathways, organelles, etc) and also in their behavior. It is within this setting that one seeks to build an integrated view of the system under study, as soon as the relevant units and the complex inter- and intra-level relationships in which these units participate have been characterized.

Abstract Motivation: Genome-wide functional annotation either by manual or automatic means has raised considerable concerns regarding the accuracy of assignments and the reproducibility of methodologies. In addition, a performance evaluation of automated systems that attempt to tackle sequence analyses rapidly and reproducibly is generally missing. In order to quantify the accuracy and reproducibility of function assignments on a genome-wide scale, we have re-annotated the entire genome sequence of Chlamydia trachomatis (serovar D), in a collaborative manner. Results: We have encoded all annotations in a structured format to allow further comparison and data exchange and have used a scale that records the different levels of potential annotation errors according to their propensity to propagate in the database due to transitive function assignments. We conclude that genome annotation may entail a considerable amount of errors, ranging from simple typographical errors to complex sequence analysis problems. The most surprising result of this comparative study is that automatic systems might perform as well as the teams of experts annotating genome sequences. Availability and supplementary information: http://www.ebi.ac.uk/research/cgg/annotation/cteval/ Contact: [email protected] * To whom correspondence should be addressed. † INA-EKETA, GR-57001 Thessaloniki, Greece ‡ Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA § Aetion Technologies LLC, Worthington, OH 43085, USA ¶ Institut Curie, F-75248 Paris, France ∥ CNRS, UMR6543, F-06108 Nice, France ** Alma Bioinformatics, E-28760 Madrid, Spain †† Cap Gemini Ernst & Young, London SW1X 7LX, UK ‡‡ Univ. of Rome ‘La Sapienza’, I-00185 Rome, Italy §§ MWG-Biotech AG, Ebersberg, D-85560 Berlin, Germany ¶¶ Wellcome Trust Biocentre, Univ. of Dundee, Dundee DD1 5HN, UK

Presents a method for finding patterns in 3D graphs. Each node in a graph is an undecomposable or atomic unit and has a label. Edges are links between the atomic units. Patterns are rigid substructures that may occur in a graph after allowing for an arbitrary number of whole-structure rotations and translations as well as a small number (specified by the user) of edit operations in the patterns or in the graph. (When a pattern appears in a graph only after the graph has been modified, we call that appearance "approximate occurrence.") The edit operations include relabeling a node, deleting a node and inserting a node. The proposed method is based on the geometric hashing technique, which hashes node-triplets of the graphs into a 3D table and compresses the label-triplets in the table. To demonstrate the utility of our algorithms, we discuss two applications of them in scientific data mining. First, we apply the method to locating frequently occurring motifs in two families of proteins pertaining to RNA-directed DNA polymerase and thymidylate synthase and use the motifs to classify the proteins. Then, we apply the method to clustering chemical compounds pertaining to aromatic compounds, bicyclicalkanes and photosynthesis. Experimental results indicate the good performance of our algorithms and high recall and precision rates for both classification and clustering.

Article Sequence homology detection through large scale pattern discovery Share on Authors: Aris Floratos Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY and Courant Institute of Mathematical Sciences, New York University, New York, NY Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY and Courant Institute of Mathematical Sciences, New York University, New York, NYView Profile , Isidore Rigoutsos Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NYView Profile , Laxmi Parida Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY and Courant Institute of Mathematical Sciences, New York University, New York, NY Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY and Courant Institute of Mathematical Sciences, New York University, New York, NYView Profile , Gustova Stolovitzky Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NYView Profile , Yuan Gao Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NYView Profile Authors Info & Claims RECOMB '99: Proceedings of the third annual international conference on Computational molecular biologyApril 1999 Pages 164–173https://doi.org/10.1145/299432.299477Online:01 April 1999Publication History 6citation379DownloadsMetricsTotal Citations6Total Downloads379Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We derive and discuss a set of parametric equations which, when given a convex 3D feature domain, K, will generate affine invariants with the property that the invariants' values are uniformly distributed in the region [0,1]/spl times/[0,1]/spl times/[0,1]. Once the shape of the feature domain K is determined and fixed it is straightforward to compute the values of the parameters and thus the proposed scheme can be tuned to a specific feature domain. The features of all recognizable objects (models) are assumed to be three-dimensional points and uniformly distributed over K. The scheme leads to improved discrimination power, improved computational-load and storage-load balancing and can also be used to determine and identify biases in the database of recognizable models (over-represented constructs of object points). Obvious enhancements produce rigid-transformation and similarity-transformation invariants with the same good distribution properties, making this approach generally applicable.

We derive and discuss a set of parametric equations which, when given a convex 2D feature domain, K, will generate affine invariants with the property that the invariants' values are uniformly distributed in the region [0,1]/spl times/[0,1]. Definition of the shape of the convex domain K allows computation of the parameters' values and thus the proposed scheme can be tuned to a specific feature domain. The features of all recognizable objects (models) are assumed to be two-dimensional points and uniformly distributed over K. The scheme leads to improved discrimination power, improved computational-load and storage-load balancing and can also be used to determine and identify biases in the database of recognizable models (over-represented constructs of object points). Obvious enhancements produce rigid-transformation and similarity-transformation invariants with the same good distribution properties, making this approach generally applicable. An extension to the case of affine invariants for feature points in three-dimensional space, with the invariants now being uniformly distributed in the region [0,1]/spl times/[0,1]/spl times/[0,1], has also been carried out and is discussed briefly. We present results for several 2D convex domains using both synthetic data and real databases.

MOTIVATION: The discovery of motifs in biological sequences is an important problem. RESULTS: This paper presents a new algorithm for the discovery of rigid patterns (motifs) in biological sequences. Our method is combinatorial in nature and able to produce all patterns that appear in at least a (user-defined) minimum number of sequences, yet it manages to be very efficient by avoiding the enumeration of the entire pattern space. Furthermore, the reported patterns are maximal: any reported pattern cannot be made more specific and still keep on appearing at the exact same positions within the input sequences. The effectiveness of the proposed approach is showcased on a number of test cases which aim to: (i) validate the approach through the discovery of previously reported patterns; (ii) demonstrate the capability to identify automatically highly selective patterns particular to the sequences under consideration. Finally, experimental analysis indicates that the algorithm is output sensitive, i.e. its running time is quasi-linear to the size of the generated output.

Increasingly larger data sets are being stored in networked architectures. Many of the available data structures are not easily amenable to parallel realizations. Hashing schemes show promise in that respect for the simple reason that the underlying data structure can be decomposed and spread among the set of cooperating nodes with minimal communication and maintenance requirements. In all cases, storage utilization and load balancing are issues that need to be addressed. One can identify two basic approaches to tackle the problem. One way is to address it as part of the design of the data structure that is used to store and retrieve the data. The other is to maintain the data structure intact but address the problem separately. The method that we present here falls in the latter category and is applicable whenever a hash table is the preferred data structure. Intrinsically attached to the used hash table is a hashing function that allows one to partition a possibly unbounded set of data items into a finite set of groups; the hashing function provides the partitioning by assigning each data item to one of the groups. In general, the hashing function cannot guarantee that the various groups will have the same cardinality on average, for all possible data item distributions. In this paper, we propose a two-stage methodology that uses the knowledge of the hashing function to reorganize the group assignments so that the resulting groups have similar expected cardinalities. The method is generally applicable and independent of the used hashing function. We show the power of the methodology using both synthetic and real-world databases. The derived quasi-uniform storage occupancy and associated load-balancing gains are significant.

Geometric hashing methods provide an efficient approach to indexing from image features into a database of models. The hash functions that have typically been used involve quantization of the values, which can result in nongraceful degradation of the performance of the system in the presence of noise. Intuitively, it is desirable to replace the quantization of hash values and the resulting binning of hash entries by a method that gives increasingly less weight to a hash table entry as a hashed feature becomes more distant from the hash entry position. In this paper, we show how these intuitive notions can be translated into a well-founded Bayesian approach to object recognition and give precise formulas for the optimal weight functions that should be used in hash space. These extensions allow the geometric hashing method to be viewed as a Bayesian maximum-likelihood framework. We demonstrate the validity of the approach by performing similarity-invariant object recognition using models obtained from drawings of military aircraft and automobiles and test images from real-world grayscale images of the same aircraft and automobile types. Our experimental results represent a complete object recognition system, since the feature extraction process is automated. Our system is scalable and works rapidly and very efficiently on an 8K-processor CM - 2, and the quality of results using similarity-invariant model matching is excellent.

A key issue in managing large amounts of data is the availability of efficient, accurate, ad selective techniques to detect homology (similarity) between newly recovered and previously acquired sequences. The algorithm presented is based on a probabilistic indexing framework which requires minimal access to the database for each match. A highly redundant number of descriptive tuples from the sequences of interest are generated and used as indices in a table look-up paradigm. Theoretical and experimental results on the sensitivity and accuracy of the approach are provided. These include the probability of correct and random matches and the storage and computational requirements. An experimental system is implemented for a database containing the complete genome of the bacteria E. Coli (approximately 2 million nucleotides). Search time is a few seconds on a workstation class machine. The algorithm is shown to scale well to databases containing billions of nucleotides with performances that are orders of magnitude better than the fastest of the current techniques.>

A new paradigm for performing realistic object recognition is presented. It is shown how several intuitive notions in the context of geometric hashing can be translated into a well-founded Bayesian approach to object recognition. This interpretation leads to well-justified formulas and gives a precise weighted-voting method for the evidence-gathering phase of geometric hashing. A computational model for performing object recognition in a distributed fashion is described. The validity of the authors' paradigm is demonstrated by presenting a prototype system that has been implemented on a small cluster of nondedicated workstations. The resulting system is scalable and can recognize models subjected to 2-D rotation, translation and scale changes in real-world digital imagery. The performance of the system is superior by a factor of 2 to that obtained for a similar system on the Connection Machine-2 (CM-2).>

A special-purpose point clustering algorithm is described, and its application to automatic grid generation, a technique used to solve partial differential equations, is considered. Extensions of techniques common in computer vision and pattern recognition literature are used to partition points into a set of enclosing rectangles. Examples from 2-D calculations are shown, but the algorithm generalizes readily to three dimensions.>