bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel Unsupervised Feature Filtering of Biological Data
Authors: Varshavsky Roy , Gottlieb Assaf , Linial Michal , Horn David 
Published: July 2006

Abstract
Motivation: Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data collected for each feature, or the projection of the feature on the first principal component. We propose a novel unsupervised criterion, based on SVD-entropy, selecting a feature according to its contribution to the entropy (CE) calculated on a leave-one-out basis. This can be implemented in four ways: simple ranking according to CE values (SR); forward selection by accumulating features according to which set produces highest entropy (FS1); forward selection by accumulating features through the choice of the best CE out of the remaining ones (FS2); backward elimination (BE) of features with the lowest CE.
Results: We apply our methods to different benchmarks. In each case we evaluate the success of clustering the data in the selected feature spaces, by measuring Jaccard scores with respect to known classifications. We demonstrate that feature filtering according to CE outperforms the variance method and gene-shaving. There are cases where the analysis, based on a small set of selected features, outperforms the best score reported when all information was used. Our method calls for an optimal size of the relevant feature set. This turns out to be just a few percents of the number of genes in the two Leukemia datasets that we have analyzed. Moreover, the most favored selected genes turn out to have significant GO enrichment in relevant cellular processes.
Abbreviations: Singular Value Decomposition (SVD), Principal Component Analysis (PCA), Quantum Clustering (QC), Gene Shaving (GS), Variance Selection (VS), Backward Elimination (BE)
Contact: royke@cs.huji.ac.il
Conflicts of Interest: not reported
Comparative footprinting of DNA-binding proteins
Authors: Contreras-Moreira Bruno , Collado-Vides Julio 
Published: July 2006

Abstract
Motivation: Comparative modelling is a computational method used to tackle a variety of problems in molecular biology and biotechnology. Traditionally it has been applied to model the structure of proteins on their own or bound to small ligands, although more recently it has also been used to model protein-protein interfaces. This work is the first to systematically analyze whether comparative models of protein-DNA complexes could be built and be useful for predicting DNA binding sites.
Results: First, we describe the structural and evolutionary conservation of protein-DNA interfaces, and the limits they impose on modelling accuracy. Second, we find that side-chains from contacting residues can be reasonably modeled and therefore used to identify contacting nucleotides. Third, the DNASITE protocol is implemented and different parameters are benchmarked on a set of 85 regulators from Escherichia coli. Results show that comparative footprinting can make useful predictions based solely on structural data, depending primarily on the interface identity with respect to the template used.
Availability: DNASITE code available on request from the authors
Contact: contrera@ccg.unam.mx
Supplementary information:
DynaPred: A structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations
Authors: Antes Iris , Siu Shirley W. I. , Lengauer Thomas 
Published: July 2006

Abstract
Motivation: The binding of endogenous antigenic peptides to MHC class I molecules is an important step during the immunologic response of a host against a pathogen. Thus, various sequence- and structure-based prediction methods have been proposed for this purpose. The sequence-based methods are computationally efficient, but are hampered by the need of sufficient experimental data and do not provide a structural interpretation of their results. The structural methods are data-independent, but are quite time-consuming and thus not suited for screening of whole genomes. Here, we present a new method, which performs sequence-based prediction by incorporating information obtained from molecular modeling. This allows us to perform large databases screening and to provide structural information of the results.
Results: We developed a SVM-trained, quantitative matrix-based method for the prediction of MHC class I binding peptides, in which the features of the scoring matrix are energy terms retrieved from molecular dynamics simulations. At the same time we used the equilibrated structures obtained from the same simulations in a simple and efficient docking procedure. Our method consists of two steps: First, we predict potential binders from sequence data alone and second, we construct protein-peptide complexes for the predicted binders. So far, we tested our approach on the HLA-A0201 allele. We constructed two prediction models, using local, position-dependent (DynaPredPOS) and global, position-independent (DynaPred) features. The former model outperformed the two sequence-based methods used in our evaluation; the latter shows a much higher generalizability towards other alleles than the position-dependent models. The constructed peptide structures can be refined within seconds to structures with an average backbone RMSD of 1.53 Å from the corresponding experimental structures.
Contact: antes@mpi-sb.mpg.de
An experimental metagenome data management and analysis system
Authors: Markowitz Victor M. , Ivanova Natalia , Palaniappan Krishna , Szeto Ernest , Korzeniewski Frank , Lykidis Athanasios , Anderson Iain , Mavrommatis Konstantinos , Kunin Victor , Garcia Martin Hector , Dubchak Inna , Hugenholtz Phil , Kyrpides Nikos C. 
Published: July 2006

Abstract
The application of shotgun sequencing to environmental samples has revealed a new universe of microbial community genomes (metagenomes) involving previously uncultured organisms. Metagenome analysis, which is expected to provide a comprehensive picture of the gene functions and metabolic capacity for microbial communities, needs to be conducted in the context of a comprehensive data management and analysis system. We present in this paper IMG/M, an experimental metagenome data management and analysis system that is based on the Integrated Microbial Genomes (IMG) system. IMG/M provides tools and viewers for analyzing both metagenomes and isolate genomes individually or in a comparative context. IMG/M is available at .
Contact: vmmarkowitz@lbl.gov
The iRMSD: a local measure of sequence alignment accuracy using structural information
Authors: Armougom Fabrice , Moretti Sébastien , Keduas Vladimir , Notredame Cedric 
Published: July 2006

Abstract
Motivation: We introduce the iRMSD, a new type of RMSD, independent from any structure superposition and suitable for evaluating sequence alignments of proteins with known structures.
Results: We demonstrate that the iRMSD is equivalent to the standard RMSD although much simpler to compute and we also show that it is suitable for comparing sequence alignments and benchmarking multiple sequence alignment methods. We tested the iRMSD score on 6 established multiple sequence alignment packages and found the results to be consistent with those obtained using an established reference alignment collection like Prefab.
Availability: The iRMSD is part of the T-Coffee package and is distributed as an open source freeware ().
Contact: cedric.notredame@europe.com; cedric.notredame@igs.cnrs-mrs.fr
Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE
Authors: Foat Barrett C. , Morozov Alexandre V. , Bussemaker Harmen J. 
Published: July 2006

Abstract
Motivation: Regulation of gene expression by a transcription factor requires physical interaction between the factor and the DNA, which can be described by a statistical mechanical model. Based on this model, we developed the MatrixREDUCE algorithm, which uses genome-wide occupancy data for a transcription factor (e.g. ChIP-chip) and associated nucleotide sequences to discover the sequence-specific binding affinity of the transcription factor. Advantages of our approach are that the information for all probes on the microarray is efficiently utilized because there is no need to delineate “bound” and “unbound” sequences, and that, unlike information content-based methods, it does not require a background sequence model.
Results: We validated the performance of MatrixREDUCE by inferring the sequence-specific binding affinities for several transcription factors in S. cerevisiae and comparing the results with three other independent sources of transcription factor sequence-specific affinity information: (i) experimental measurement of transcription factor binding affinities for specific oligonucleotides, (ii) reporter gene assays for promoters with systematically mutated binding sites, and (iii) relative binding affinities obtained by modeling transcription factor-DNA interactions based on co-crystal structures of transcription factors bound to DNA substrates. We show that transcription factor binding affinities inferred by MatrixREDUCE are in good agreement with all three validating methods.
Availability: MatrixREDUCE source code is freely available for non-commercial use at . The software runs on Linux, Unix, and Mac OS X.
Contact: Harmen.Bussemaker@columbia.edu
On counting position weight matrix matches in a sequence, with application to discriminative motif finding
Authors: Sinha Saurabh 
Published: July 2006

Abstract
Motivation and Results: The position weight matrix (PWM) is a popular method to model transcription factor binding sites. A fundamental problem in cis-regulatory analysis is to “count” the occurrences of a PWM in a DNA sequence. We propose a novel probabilistic score to solve this problem of counting PWM occurrences. The proposed score has two important properties: (1) It gives appropriate weights to both strong and weak occurrences of the PWM, without using thresholds. (2) For any given PWM, this score can be computed while allowing for occurrences of other, a priori known PWMs, in a statistically sound framework. Additionally, the score is efficiently differentiable with respect to the PWM parameters, which has important consequences for designing search algorithms.
The second problem we address is to find, ab initio, PWMs that have high counts in one set of sequences, and low counts in another. We develop a novel algorithm to solve this “discriminative motif-finding problem”, using the proposed score for counting a PWM in the sequences. The algorithm is a local search technique that exploits derivative information on an objective function to enhance speed and performance. It is extensively tested on synthetic data, and shown to perform better than other discriminative as well as non-discriminative PWM finding algorithms. It is then applied to cis-regulatory modules involved in development of the fruitfly embryo, to elicit known and novel motifs. We finally use the algorithm on genes predictive of social behavior in the honey bee, and find interesting motifs.
Availability: The program is available upon request from the author.
Contact: sinhas@cs.uiuc.edu
Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles
Authors: Edelman Elena , Porrello Alessandro , Guinney Justin , Balakumaran Bala , Bild Andrea , Febbo Phillip G. , Mukherjee Sayan 
Published: July 2006

Abstract
Motivation: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies.
Results: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores.
Availability: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at
Contact: sayan@stat.duke.edu
A sequence-based filtering method for ncRNA identification and its application to searching for riboswitch elements
Authors: Zhang Shaojie , Borovok Ilya , Aharonowitz Yair , Sharan Roded , Bafna Vineet 
Published: July 2006

Abstract
Motivation: Recent studies have uncovered an “RNA world”, in which non coding RNA (ncRNA) sequences play a central role in the regulation of gene expression. Computational studies on ncRNA have been directed toward developing detection methods for ncRNAs. State-of-the-art methods for the problem, like covariance models, suffer from high computational cost, underscoring the need for efficient filtering approaches that can identify promising sequence segments and speedup the detection process.
Results: In this paper we make several contributions toward this goal. First, we formalize the concept of a filter and provide figures of merit that allow comparison between filters. Second, we design efficient sequence based filters that dominate the current state-of-the-art HMM filters. Third, we provide a new formulation of the covariance model that allows speeding up RNA alignment. We demonstrate the power of our approach on both synthetic data and real bacterial genomes. We then apply our algorithm to the detection of novel riboswitch elements from the whole bacterial and archaeal genomes. Our results point to a number of novel riboswitch candidates, and include genomes that were not previously known to contain riboswitches.
Availability: The program is available upon request from the authors.
Contact: shzhang@cs.ucsd.edu
A computational approach toward label-free protein quantification using predicted peptide detectability
Authors: Tang Haixu , Arnold Randy J. , Alves Pedro , Xun Zhiyin , Clemmer David E. , Novotny Milos V. , Reilly James P. , Radivojac Predrag 
Published: July 2006

Abstract
Summary: We propose here a new concept of peptide detectability which could be an important factor in explaining the relationship between a protein's quantity and the peptides identified from it in a high-throughput proteomics experiment. We define peptide detectability as the probability of observing a peptide in a standard sample analyzed by a standard proteomics routine and argue that it is an intrinsic property of the peptide sequence and neighboring regions in the parent protein. To test this hypothesis we first used publicly available data and data from our own synthetic samples in which quantities of model proteins were controlled. We then applied machine learning approaches to demonstrate that peptide detectability can be predicted from its sequence and the neighboring regions in the parent protein with satisfactory accuracy. The utility of this approach for protein quantification is demonstrated by peptides with higher detectability generally being identified at lower concentrations over those with lower detectability in the synthetic protein mixtures. These results establish a direct link between protein concentration and peptide detectability. We show that for each protein there exists a level of peptide detectability above which peptides are detected and below which peptides are not detected in an experiment. We call this level the minimum acceptable detectability for identified peptides (MDIP) which can be calibrated to predict protein concentration. Triplicate analysis of a biological sample showed that these MDIP values are consistent among the three data sets.
Contact: predrag@indiana.edu
Integrating copy number polymorphisms into array CGH analysis using a robust HMM
Authors: Shah Sohrab P. , Xuan Xiang , DeLeeuw Ron J. , Khojasteh Mehrnoush , Lam Wan L. , Ng Raymond , Murphy Kevin P. 
Published: July 2006

Abstract
Motivation: Array comparative genomic hybridization (aCGH) is a pervasive technique used to identify chromosomal aberrations in human diseases, including cancer. Aberrations are defined as regions of increased or decreased DNA copy number, relative to a normal sample. Accurately identifying the locations of these aberrations has many important medical applications. Unfortunately, the observed copy number changes are often corrupted by various sources of noise, making the boundaries hard to detect. One popular current technique uses hidden Markov models (HMMs) to divide the signal into regions of constant copy number called segments; a subsequent classification phase labels each segment as a gain, a loss or neutral. Unfortunately, standard HMMs are sensitive to outliers, causing over-segmentation, where segments erroneously span very short regions.
Results: We propose a simple modification that makes the HMM robust to such outliers. More importantly, this modification allows us to exploit prior knowledge about the likely location of “outliers”, which are often due to copy number polymorphisms (CNPs). By “explaining away” these outliers with prior knowledge about the locations of CNPs, we can focus attention on the more clinically relevant aberrated regions. We show significant improvements over the current state of the art technique (DNAcopy with MergeLevels) on previously published data from mantle cell lymphoma cell lines, and on published benchmark synthetic data augmented with outliers.
Availability: Source code written in Matlab is available from .
Contact: sshah@cs.ubc.ca
Mutation parameters from DNA sequence data using graph theoretic measures on lineage trees
Authors: Magori-Cohen Reuma , Louzoun Yoram , Kleinstein Steven H. 
Published: July 2006

Abstract
Motivation: B cells responding to antigenic stimulation can fine-tune their binding properties through a process of affinity maturation composed of somatic hypermutation, affinity-selection and clonal expansion. The mutation rate of the B cell receptor DNA sequence, and the effect of these mutations on affinity and specificity, are of critical importance for understanding immune and autoimmune processes. Unbiased estimates of these properties are currently lacking due to the short time-scales involved and the small numbers of sequences available.
Results: We have developed a bioinformatic method based on a maximum likelihood analysis of phylogenetic lineage trees to estimate the parameters of a B cell clonal expansion model, which includes somatic hypermutation with the possibility of lethal mutations. Lineage trees are created from clonally related B cell receptor DNA sequences. Important links between tree shapes and underlying model parameters are identified using mutual information. Parameters are estimated using a likelihood function based on the joint distribution of several tree shapes, without requiring a priori knowledge of the number of generations in the clone (which is not available for rapidly dividing populations in vivo). A systematic validation on synthetic trees produced by a mutating birth-death process simulation shows that our estimates are precise and robust to several underlying assumptions. These methods are applied to experimental data from autoimmune mice to demonstrate the existence of hypermutating B cells in an unexpected location in the spleen.
Contact: stevenk@cs.princeton.edu
SNP Function Portal: a web database for exploring the function implication of SNP alleles
Authors: Wang Pinglang , Dai Manhong , Xuan Weijian , McEachin Richard C. , Jackson Anne U. , Scott Laura J. , Athey Brian , Watson Stanley J. , Meng Fan 
Published: July 2006

Abstract
Motivation: Finding the potential functional significance of SNPs is a major bottleneck in understanding genome-wide SNP scanning results, as the related functional data are distributed across many different databases. The SNP Function Portal is designed to be a clearing house for all public domain SNP functional annotation data, as well as in-house functional annotations derived from different data sources. It currently contains SNP functional annotations in six major categories including genomic elements, transcription regulation, protein function, pathway, disease and population genetics. Besides extensive SNP functional annotations, the SNP Function Portal includes a powerful search engine that accepts different types of genetic markers as input and identifies all genetically related SNPs based on the HapMap Phase II data as well as the relationship of different markers to known genes. As a result, our system allows users to identify the potential biological impact of genetic markers and complex relationships among genetic markers and genes, and it greatly facilitates knowledge discovery in genome-wide SNP scanning experiments.
Availability:
Contact: mengf@umich.edu
MotifCut: regulatory motifs finding with maximum density subgraphs
Authors: Fratkin Eugene , Naughton Brian T. , Brutlag Douglas L. , Batzoglou Serafim 
Published: July 2006

Abstract
Motivation: DNA motif finding is one of the core problems in computational biology, for which several probabilistic and discrete approaches have been developed. Most existing methods formulate motif finding as an intractable optimization problem and rely either on expectation maximization (EM) or on local heuristic searches. Another challenge is the choice of motif model: simpler models such as the position-specific scoring matrix (PSSM) impose biologically unrealistic assumptions such as independence of the motif positions, while more involved models are harder to parametrize and learn.
Results: We present MotifCut, a graph-theoretic approach to motif finding leading to a convex optimization problem with a polynomial time solution. We build a graph where the vertices represent all k-mers in the input sequences, and edges represent pairwise k-mer similarity. In this graph, we search for a motif as the maximum density subgraph, which is a set of k-mers that exhibit a large number of pairwise similarities. Our formulation does not make strong assumptions regarding the structure of the motif and in practice both motifs that fit well the PSSM model, and those that exhibit strong dependencies between position pairs are found as dense subgraphs. We benchmark MotifCut on both synthetic and real yeast motifs, and find that it compares favorably to existing popular methods. The ability of MotifCut to detect motifs appears to scale well with increasing input size. Moreover, the motifs we discover are different from those discovered by the other methods.
Availability: MotifCut server and other materials can be found at motifcut.stanford.edu
Contact: fratkin@cs.stanford.edu