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arXiv2010

Mining Spatial Gene Expression Data Using Negative Association Rules

Over the years, data mining has attracted most of the attention from the research community. The researchers attempt to develop faster, more scalable algorithms to navigate over the ever increasing volumes of spatial gene expression data in search of meaningful patterns. Association rules are a data mining technique that tries to identify intrinsic patterns in spatial gene expression data. It has been widely used in different applications, a lot of algorithms introduced to discover these rules. However Priori like algorithms has been used to find positive association rules. In contrast to positive rules, negative rules encapsulate relationship between the occurrences of one set of items with absence of the other set of items. In this paper, an algorithm for mining negative association rules from spatial gene expression data is introduced. The algorithm intends to discover the negative association rules which are complementary to the association rules often generated by Priori like algorithm. Our study shows that negative association rules can be discovered efficiently from spatial gene expression data.

cs.DB↗

arXiv2010

Increasing stability and interpretability of gene expression signatures

Motivation : Molecular signatures for diagnosis or prognosis estimated from large-scale gene expression data often lack robustness and stability, rendering their biological interpretation challenging. Increasing the signature's interpretability and stability across perturbations of a given dataset and, if possible, across datasets, is urgently needed to ease the discovery of important biological processes and, eventually, new drug targets. Results : We propose a new method to construct signatures with increased stability and easier interpretability. The method uses a gene network as side interpretation and enforces a large connectivity among the genes in the signature, leading to signatures typically made of genes clustered in a few subnetworks. It combines the recently proposed graph Lasso procedure with a stability selection procedure. We evaluate its relevance for the estimation of a prognostic signature in breast cancer, and highlight in particular the increase in interpretability and stability of the signature.

stat.ML↗

arXiv2010

Interestingness Measure for Mining Spatial Gene Expression Data using Association Rule

The search for interesting association rules is an important topic in knowledge discovery in spatial gene expression databases. The set of admissible rules for the selected support and confidence thresholds can easily be extracted by algorithms based on support and confidence, such as Apriori. However, they may produce a large number of rules, many of them are uninteresting. The challenge in association rule mining (ARM) essentially becomes one of determining which rules are the most interesting. Association rule interestingness measures are used to help select and rank association rule patterns. Besides support and confidence, there are other interestingness measures, which include generality reliability, peculiarity, novelty, surprisingness, utility, and applicability. In this paper, the application of the interesting measures entropy and variance for association pattern discovery from spatial gene expression data has been studied. In this study the fast mining algorithm has been used which produce candidate itemsets and it spends less time for calculating k-supports of the itemsets with the Boolean matrix pruned, and it scans the database only once and needs less memory space. Experimental results show that using entropy as the measure of interest for the spatial gene expression data has more diverse and interesting rules.

cs.DB↗

arXiv2012

How and where to look for tRNAs in Metazoan mitochondrial genomes, and what you might find when you get there

The ability to locate and annotate mitochondrial genes is an important practical issue, given the rapidly increasing number of mitogenomes appearing in the public databases. Unfortunately, tRNA genes in Metazoan mitochondria have proved to be problematic because they often vary in number (genes missing or duplicated) and also in the secondary structure of the transcribed tRNAs (T or D arms missing). I have performed a series of comparative analyses of the tRNA genes of a broad range of Metazoan mitogenomes in order to address this issue. I conclude that no single computer program is necessarily capable of finding all of the tRNA genes in any given mitogenome, and that use of both the ARWEN and DOGMA programs is sometimes necessary because they produce complementary false negatives. There are apparently a very large number of erroneous annotations in the databased mitogenome sequences, including missed genes, wrongly annotated locations, false complements, and inconsistent criteria for assigning the 5' and 3' boundaries; and I have listed many of these. The extent of overlap between genes is often greatly exaggerated due to inconsistent annotations, although notable overlaps involving tRNAs are apparently real. Finally, three novel hypotheses were examined and found to have support from the comparative analyses: (1) some organisms have mitogenomic locations that simultaneously code for multiple tRNAs; (2) some organisms have mitogenomic locations that simultaneously code for tRNAs and proteins (but not rRNAs); and (3) one group of nematodes has several genes that code for tRNAs lacking both the D and T arms.

q-bio.GN↗

arXiv2010

BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies

Gene-gene interactions have long been recognized to be fundamentally important to understand genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named `BOolean Operation based Screening and Testing'(BOOST). To discover unknown gene-gene interactions that underlie complex diseases, BOOST allows examining all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hours on a standard 3.0 GHz desktop with 4G memory running Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, while both data sets share a very similar hit region in the WTCCC report. BOOST has also identified many undiscovered interactions between genes in the major histocompatibility complex (MHC) region in the type 1 diabetes data set. In the coming era of large-scale interaction mapping in genome-wide case-control studies, our method can serve as a computationally and statistically useful tool.

q-bio.GN↗

arXiv2010

Automated DNA Motif Discovery

Ensembl's human non-coding and protein coding genes are used to automatically find DNA pattern motifs. The Backus-Naur form (BNF) grammar for regular expressions (RE) is used by genetic programming to ensure the generated strings are legal. The evolved motif suggests the presence of Thymine followed by one or more Adenines etc. early in transcripts indicate a non-protein coding gene. Keywords: pseudogene, short and microRNAs, non-coding transcripts, systems biology, machine learning, Bioinformatics, motif, regular expression, strongly typed genetic programming, context-free grammar.

q-bio.BM↗

arXiv2010

Prediction of RNA-RNA interaction structure by centroids in the Boltzmann ensemble

New high-throughput sequencing technologies have made it possible to pursue the advent of genome-wide transcriptomics. That progress combined with the recent discovery of regulatory non-coding RNAs (ncRNAs) has necessitated fast and accurate algorithms to predict RNA-RNA interaction probability and structure. Although there are algorithms to predict minimum free energy interaction secondary structure for two nucleic acids, little work has been done to exploit the information invested in the base pair probabilities to improve interaction structure prediction. In this paper, we present an algorithm to predict the Hamming centroid of the Boltzmann ensemble of interaction structures. We also present an efficient algorithm to sample interaction structures from the ensemble. Our sampling algorithm uses a balanced scheme for traversing indices which improves the running time of the Ding-Lawrence sampling algorithm. The Ding-Lawrence sampling algorithm has $O(n^2m^2)$ time complexity whereas our algorithm has $O((n+m)^2\log(n+m))$ time complexity, in which $n$ and $m$ are the lengths of input strands. We implemented our algorithm in a new version of {\tt piRNA} and compared our structure prediction results with competitors. Our centroid prediction outperforms competitor minimum-free-energy prediction algorithms on average.

q-bio.BM↗

arXiv2010

BAK1 Gene Variation: the doubts remain

Dr. Hatchwell [2010] has proposed that the BAK1 gene variants were likely due to sequencing of a processed gene on chromosome 20. However, in response, Dr. Gottlieb and co-authors [2010] have argued that "some but not all of the sequence changes present in the BAK1 sequence of our abdominal aorta samples are also present in the chromosome 20 BAK1 sequence. However, all the AAA and AA cDNA samples are identical to each other and different from chromosome 20 BAK1 sequence at amino acids 2 and 145". I have been following this discussion because I have independently reached almost the same conclusion as Dr. Hatchwell did [Yamagishi, 2009], and, unfortunately, the response from Dr. Gottlieb and his co-authors seems to me to be unsatisfactory for the reasons listed below

q-bio.GN↗

arXiv2011

Partition Decoupling for Multi-gene Analysis of Gene Expression Profiling Data

We present the extention and application of a new unsupervised statistical learning technique--the Partition Decoupling Method--to gene expression data. Because it has the ability to reveal non-linear and non-convex geometries present in the data, the PDM is an improvement over typical gene expression analysis algorithms, permitting a multi-gene analysis that can reveal phenotypic differences even when the individual genes do not exhibit differential expression. Here, we apply the PDM to publicly-available gene expression data sets, and demonstrate that we are able to identify cell types and treatments with higher accuracy than is obtained through other approaches. By applying it in a pathway-by-pathway fashion, we demonstrate how the PDM may be used to find sets of mechanistically-related genes that discriminate phenotypes.

q-bio.QM↗

arXiv2010

CGHTRIMMER: Discretizing noisy Array CGH Data

The development of cancer is largely driven by the gain or loss of subsets of the genome, promoting uncontrolled growth or disabling defenses against it. Identifying genomic regions whose DNA copy number deviates from the normal is therefore central to understanding cancer evolution. Array-based comparative genomic hybridization (aCGH) is a high-throughput technique for identifying DNA gain or loss by quantifying total amounts of DNA matching defined probes relative to healthy diploid control samples. Due to the high level of noise in microarray data, however, interpretation of aCGH output is a difficult and error-prone task. In this work, we tackle the computational task of inferring the DNA copy number per genomic position from noisy aCGH data. We propose CGHTRIMMER, a novel segmentation method that uses a fast dynamic programming algorithm to solve for a least-squares objective function for copy number assignment. CGHTRIMMER consistently achieves superior precision and recall to leading competitors on benchmarks of synthetic data and real data from the Coriell cell lines. In addition, it finds several novel markers not recorded in the benchmarks but plausibly supported in the oncology literature. Furthermore, CGHTRIMMER achieves superior results with run-times from 1 to 3 orders of magnitude faster than its state-of-art competitors. CGHTRIMMER provides a new alternative for the problem of aCGH discretization that provides superior detection of fine-scale regions of gain or loss yet is fast enough to process very large data sets in seconds. It thus meets an important need for methods capable of handling the vast amounts of data being accumulated in high-throughput studies of tumor genetics.

q-bio.GN↗

arXiv2010

Markov Logic Networks in the Analysis of Genetic Data

Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of partial influences of each individual gene and can often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying genotype-phenotype mechanisms. Modeling approaches from the AI field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), as a framework for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we have been able to replicate the results of traditional statistical methods. Moreover, we show that even with simple models we are able to go beyond finding independent markers linked to a phenotype by using joint inference that avoids an independence assumption. The method is applied to genetic data on yeast sporulation, a phenotype governed by non-linear gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method is able to identify four loci with small effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics.

q-bio.GN↗

arXiv2010

Model for Diversity Analysis of Antigen Receptor Repertoires

In most of the recent immunological literature the differences across antigen receptor populations are examined via non-parametric statistical measures of species overlap and diversity borrowed from ecological studies. While this approach is robust in a wide range of situations, it seems to provide little insight into the underlying clonal size distribution and the overall mechanism differentiating the receptor populations. As a possible alternative, the current paper presents a parametric method which adjusts for the data under-sampling as well as provides a unifying approach to simultaneous comparison of multiple receptor groups by means of the modern statistical tools of unsupervised learning. The parametric model is based on a flexible multivariate Poisson-lognormal distribution and is seen to be a natural generalization of the univariate Poisson-lognormal models used in ecological studies of biodiversity patterns. The procedure for evaluating model's fit is described along with the public domain software developed to perform the necessary diagnostics. The model-driven analysis is seen to compare favorably vis a vis traditional methods when applied to the data from T-cell receptors in transgenic mice populations.

q-bio.BM↗

arXiv2010

Occupancy distributions in Markov chains via Doeblin's ergodicity coefficient

We apply Doeblin's ergodicity coefficient as a computational tool to approximate the occupancy distribution of a set of states in a homogeneous but possibly non-stationary finite Markov chain. Our approximation is based on new properties satisfied by this coefficient, which allow us to approximate a chain of duration n by independent and short-lived realizations of an auxiliary homogeneous Markov chain of duration of order ln(n). Our approximation may be particularly useful when exact calculations via first-step methods or transfer matrices are impractical, and asymptotic approximations may not be yet reliable. Our findings may find applications to pattern problems in Markovian and non-Markovian sequences that are treatable via embedding techniques.

math.PR↗

arXiv2010

RNA-RNA interaction prediction based on multiple sequence alignments

Many computerized methods for RNA-RNA interaction structure prediction have been developed. Recently, $O(N^6)$ time and $O(N^4)$ space dynamic programming algorithms have become available that compute the partition function of RNA-RNA interaction complexes. However, few of these methods incorporate the knowledge concerning related sequences, thus relevant evolutionary information is often neglected from the structure determination. Therefore, it is of considerable practical interest to introduce a method taking into consideration both thermodynamic stability and sequence covariation. We present the \emph{a priori} folding algorithm \texttt{ripalign}, whose input consists of two (given) multiple sequence alignments (MSA). \texttt{ripalign} outputs (1) the partition function, (2) base-pairing probabilities, (3) hybrid probabilities and (4) a set of Boltzmann-sampled suboptimal structures consisting of canonical joint structures that are compatible to the alignments. Compared to the single sequence-pair folding algorithm \texttt{rip}, \texttt{ripalign} requires negligible additional memory resource. Furthermore, we incorporate possible structure constraints as input parameters into our algorithm. The algorithm described here is implemented in C as part of the \texttt{rip} package. The supplemental material, source code and input/output files can freely be downloaded from \url{http://www.combinatorics.cn/cbpc/ripalign.html}. \section{Contact} Christian Reidys \texttt{duck@santafe.edu}

math-ph↗

arXiv2010

Gene Expression Data Knowledge Discovery using Global and Local Clustering

To understand complex biological systems, the research community has produced huge corpus of gene expression data. A large number of clustering approaches have been proposed for the analysis of gene expression data. However, extracting important biological knowledge is still harder. To address this task, clustering techniques are used. In this paper, hybrid Hierarchical k-Means algorithm is used for clustering and biclustering gene expression data is used. To discover both local and global clustering structure biclustering and clustering algorithms are utilized. A validation technique, Figure of Merit is used to determine the quality of clustering results. Appropriate knowledge is mined from the clusters by embedding a BLAST similarity search program into the clustering and biclustering process. To discover both local and global clustering structure biclustering and clustering algorithms are utilized. To determine the quality of clustering results, a validation technique, Figure of Merit is used. Appropriate knowledge is mined from the clusters by embedding a BLAST similarity search program into the clustering and biclustering process.

cs.CE↗

arXiv2010

Towards automated high-throughput screening of C. elegans on agar

High-throughput screening (HTS) using model organisms is a promising method to identify a small number of genes or drugs potentially relevant to human biology or disease. In HTS experiments, robots and computers do a significant portion of the experimental work. However, one remaining major bottleneck is the manual analysis of experimental results, which is commonly in the form of microscopy images. This manual inspection is labor intensive, slow and subjective. Here we report our progress towards applying computer vision and machine learning methods to analyze HTS experiments that use Caenorhabditis elegans (C. elegans) worms grown on agar. Our main contribution is a robust segmentation algorithm for separating the worms from the background using brightfield images. We also show that by combining the output of this segmentation algorithm with an algorithm to detect the fluorescent dye, Nile Red, we can reliably distinguish different fluorescence-based phenotypes even though the visual differences are subtle. The accuracy of our method is similar to that of expert human analysts. This new capability is a significant step towards fully automated HTS experiments using C. elegans.

cs.CV↗

arXiv2010

pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree

Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power of likelihood-based approaches to large data sets. This paper introduces pplacer, a software package for phylogenetic placement and subsequent visualization. The algorithm can place twenty thousand short reads on a reference tree of one thousand taxa per hour per processor, has essentially linear time and memory complexity in the number of reference taxa, and is easy to run in parallel. Pplacer features calculation of the posterior probability of a placement on an edge, which is a statistically rigorous way of quantifying uncertainty on an edge-by-edge basis. It also can inform the user of the positional uncertainty for query sequences by calculating expected distance between placement locations, which is crucial in the estimation of uncertainty with a well-sampled reference tree. The software provides visualizations using branch thickness and color to represent number of placements and their uncertainty. A simulation study using reads generated from 631 COG alignments shows a high level of accuracy for phylogenetic placement over a wide range of alignment diversity, and the power of edge uncertainty estimates to measure placement confidence. Pplacer enables efficient phylogenetic placement and subsequent visualization, making likelihood-based phylogenetics methodology practical for large collections of reads; it is available as source code, binaries, and a web service.

q-bio.PE↗

arXiv2010

A simple and fast method to determine the parameters for fuzzy c-means cluster validation

Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional data sets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to determine the two parameters fuzzifier and cluster number. Wrong parameter values may either lead to the inclusion of purely random fluctuations in the results or ignore potentially important data. The optimal solution has parameter values for which the clustering does not yield any results for a purely random data set but which detects cluster formation with maximum resolution on the edge of randomness. Estimation of the optimal parameter values is achieved by evaluation of the results of the clustering procedure applied to randomized data sets. In this case, the optimal value of the fuzzifier follows common rules that depend only on the main properties of the data set. Taking the dimension of the set and the number of objects as input values instead of evaluating the entire data set allows us to propose a functional relationship determining its value directly. This result speaks strongly against setting the fuzzifier equal to 2 as typically done in many previous studies. Validation indices are generally used for the estimation of the optimal number of clusters. A comparison shows that the minimum distance between the centroids provides results that are at least equivalent or better than those obtained by other computationally more expensive indices.

q-bio.QM↗