Transcription Factor Map Alignment of Promoter Regions

We address the problem of comparing and characterizing the promoter regions of genes with similar expression patterns. This remains a challenging problem in sequence analysis, because often the promoter regions of co-expressed genes do not show discernible sequence conservation. In our approach, thus, we have not directly compared the nucleotide sequence of promoters. Instead, we have obtained predictions of transcription factor binding sites, annotated the predicted sites with the labels of the corresponding binding factors, and aligned the resulting sequences of labels—to which we refer here as transcription factor maps (TF-maps). To obtain the global pairwise alignment of two TF-maps, we have adapted an algorithm initially developed to align restriction enzyme maps. We have optimized the parameters of the algorithm in a small, but well-curated, collection of human–mouse orthologous gene pairs. Results in this dataset, as well as in an independent much larger dataset from the CISRED database, indicate that TF-map alignments are able to uncover conserved regulatory elements, which cannot be detected by the typical sequence alignments

A Macaque's-Eye View of Human Insertions and Deletions: Differences in Mechanisms

Insertions and deletions (indels) cause numerous genetic diseases and lead to pronounced evolutionary differences among genomes. The macaque sequences provide an opportunity to gain insights into the mechanisms generating these mutations on a genome-wide scale by establishing the polarity of indels occurring in the human lineage since its divergence from the chimpanzee. Here we apply novel regression techniques and multiscale analyses to demonstrate an extensive regional indel rate variation stemming from local fluctuations in divergence, GC content, male and female recombination rates, proximity to telomeres, and other genomic factors. We find that both replication and, surprisingly, recombination are significantly associated with the occurrence of small indels. Intriguingly, the relative inputs of replication versus recombination differ between insertions and deletions, thus the two types of mutations are likely guided in part by distinct mechanisms. Namely, insertions are more strongly associated with factors linked to recombination, while deletions are mostly associated with replication-related features. Indel as a term misleadingly groups the two types of mutations together by their effect on a sequence alignment. However, here we establish that the correct identification of a small gap as an insertion or a deletion (by use of an outgroup) is crucial to determining its mechanism of origin. In addition to providing novel insights into insertion and deletion mutagenesis, these results will assist in gap penalty modeling and eventually lead to more reliable genomic alignments

From Functional Genomics to Functional Immunomics: New Challenges, Old Problems, Big Rewards

The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology—a spatially addressable, large-scale technology for measurement of specific immunological response—the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models

Making open data work for plant scientists

Despite the clear demand for open data sharing, its implementation within plant science is still limited. This is, at least in part, because open data-sharing raises several unanswered questions and challenges to current research practices. In this commentary, some of the challenges encountered by plant researchers at the bench when generating, interpreting, and attempting to disseminate their data have been highlighted. The difficulties involved in sharing sequencing, transcriptomics, proteomics, and metabolomics data are reviewed. The benefits and drawbacks of three data-sharing venues currently available to plant scientists are identified and assessed: (i) journal publication; (ii) university repositories; and (iii) community and project-specific databases. It is concluded that community and project-specific databases are the most useful to researchers interested in effective data sharing, since these databases are explicitly created to meet the researchers’ needs, support extensive curation, and embody a heightened awareness of what it takes to make data reuseable by others. Such bottom-up and community-driven approaches need to be valued by the research community, supported by publishers, and provided with long-term sustainable support by funding bodies and government. At the same time, these databases need to be linked to generic databases where possible, in order to be discoverable to the majority of researchers and thus promote effective and efficient data sharing. As we look forward to a future that embraces open access to data and publications, it is essential that data policies, data curation, data integration, data infrastructure, and data funding are linked together so as to foster data access and research productivity

The Influence of Recombination on Human Genetic Diversity

In humans, the rate of recombination, as measured on the megabase scale, is positively associated with the level of genetic variation, as measured at the genic scale. Despite considerable debate, it is not clear whether these factors are causally linked or, if they are, whether this is driven by the repeated action of adaptive evolution or molecular processes such as double-strand break formation and mismatch repair. We introduce three innovations to the analysis of recombination and diversity: fine-scale genetic maps estimated from genotype experiments that identify recombination hotspots at the kilobase scale, analysis of an entire human chromosome, and the use of wavelet techniques to identify correlations acting at different scales. We show that recombination influences genetic diversity only at the level of recombination hotspots. Hotspots are also associated with local increases in GC content and the relative frequency of GC-increasing mutations but have no effect on substitution rates. Broad-scale association between recombination and diversity is explained through covariance of both factors with base composition. To our knowledge, these results are the first evidence of a direct and local influence of recombination hotspots on genetic variation and the fate of individual mutations. However, that hotspots have no influence on substitution rates suggests that they are too ephemeral on an evolutionary time scale to have a strong influence on broader scale patterns of base composition and long-term molecular evolution

Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries

<p>Abstract</p> <p>Background</p> <p>The joint analysis of several categorical variables is a common task in many areas of biology, and is becoming central to systems biology investigations whose goal is to identify potentially complex interaction among variables belonging to a network. Interactions of arbitrary complexity are traditionally modeled in statistics by log-linear models. It is challenging to extend these to the high dimensional and potentially sparse data arising in computational biology. An important example, which provides the motivation for this article, is the analysis of so-called full-length cDNA libraries of alternatively spliced genes, where we investigate relationships among the presence of various exons in transcript species.</p> <p>Results</p> <p>We develop methods to perform model selection and parameter estimation in log-linear models for the analysis of sparse contingency tables, to study the interaction of two or more factors. Maximum Likelihood estimation of log-linear model coefficients might not be appropriate because of the presence of zeros in the table's cells, and new methods are required. We propose a computationally efficient ℓ₁-penalization approach extending the Lasso algorithm to this context, and compare it to other procedures in a simulation study. We then illustrate these algorithms on contingency tables arising from full-length cDNA libraries.</p> <p>Conclusion</p> <p>We propose regularization methods that can be used successfully to detect complex interaction patterns among categorical variables in a broad range of biological problems involving categorical variables.</p

Genome re-annotation: a wiki solution?

The annotation of most genomes becomes outdated over time, owing in part to our ever-improving knowledge of genomes and in part to improvements in bioinformatics software. Unfortunately, annotation is rarely if ever updated and resources to support routine reannotation are scarce. Wiki software, which would allow many scientists to edit each genome's annotation, offers one possible solution

How repetitive are genomes?

BACKGROUND: Genome sequences vary strongly in their repetitiveness and the causes for this are still debated. Here we propose a novel measure of genome repetitiveness, the index of repetitiveness, I(r), which can be computed in time proportional to the length of the sequences analyzed. We apply it to 336 genomes from all three domains of life. RESULTS: The expected value of I(r )is zero for random sequences of any G/C content and greater than zero for sequences with excess repeats. We find that the I(r )of archaea is significantly smaller than that of eubacteria, which in turn is smaller than that of eukaryotes. Mouse chromosomes have a significantly higher I(r )than human chromosomes and within each genome the Y chromosome is most repetitive. A sliding window analysis reveals that the human HOXA cluster and two surrounding genes are characterized by local minima in I(r). A program for calculating the I(r )is freely available at . CONCLUSION: The general measure of DNA repetitiveness proposed in this paper can be efficiently computed on a genomic scale. This reveals a broad spectrum of repetitiveness among diverse genomes which agrees qualitatively with previous studies of repeat content. A sliding window analysis helps to analyze the intragenomic distribution of repeats