Towards human-computer synergetic analysis of large-scale biological data

AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected aptamers

Aptamers are short single-stranded DNA or RNA molecules with high affinity and specificity for targets and are generated using the iterative systematic evolution of ligands by exponential enrichment (SELEX) process. Next-generation sequencing (NGS) revolutionized aptamer selections by allowing a more comprehensive analysis of SELEX-enriched aptamers as compared to Sanger sequencing. The current challenge with aptamer NGS datasets is identifying a diverse cohort of candidate aptamers with the highest likelihood of successful experimental validation. Here we present AptamerRunner, an aptamer sequence and/or structure clustering algorithm that synergistically integrates computational analysis with visualization and expertise-directed decision making. The visual integration of networked aptamers with ranking data, such as fold enrichment or scoring algorithm results, represents a significant advancement over existing clustering tools by providing a natural context to depict groups of aptamers from which ranked or scored candidates can be chosen for experimental validation. The inherent flexibility, user-friendly design, and prospects for future enhancements with AptamerRunner have broad-reaching implications for aptamer researchers across a wide range of disciplines.

A functional mechanism for a non-coding variant near AGTR2 associated with risk for preterm birth

BACKGROUND

Preterm birth (PTB), defined as delivery before 37 gestational weeks, imposes significant public health burdens. A recent maternal genome-wide association study of spontaneous PTB identified a noncoding locus near the angiotensin II receptor type 2 (AGTR2) gene. Genotype-Tissue Expression data revealed that alleles associated with decreased AGTR2 expression in the uterus were linked to an increased risk of PTB and shortened gestational duration. We hypothesized that a causative variant in this locus modifies AGTR2 expression by altering transcription factor (TF) binding.

METHODS

To investigate this hypothesis, we performed bioinformatics analyses and functional characterizations at the implicated locus. Potential causal single nucleotide polymorphisms (SNPs) were prioritized, and allele-dependent binding of TFs was predicted. Reporter assays were employed to assess the enhancer activity of the top PTB-associated non-coding variant, rs7889204, and its impact on TF binding.

RESULTS

Our analyses revealed that rs7889204, a top PTB-associated non-coding genetic variant is one of the strongest eQTLs for the AGTR2 gene in uterine tissue samples. We observed differential binding of CEBPB (CCAAT enhancer binding protein beta) and HOXA10 (homeobox A10) to the alleles of rs7889204. Reporter assays demonstrated decreased enhancer activity for the rs7889204 risk "C" allele.

CONCLUSION

Collectively, these results demonstrate that decreased AGTR2 expression caused by reduced transcription factor binding increases the risk for PTB and suggest that enhancing AGTR2 activity may be a preventative measure in reducing PTB risk.

Asthma-associated comorbidities in children with and without secondhand smoke exposure

BACKGROUND

Secondhand smoke (SHS) exposure is known to trigger asthma, but asthma disease severity and comorbidities in children exposed to SHS are not very well quantified.

OBJECTIVE

To identify comorbidities and understand health care usage in children with asthma exposed to SHS (cases) compared with children with asthma but without SHS exposure (controls).

METHODS

A retrospective nested matched case-and-control study was conducted with children 5 to 18 years old who were enrolled in the Pediatric Asthma Management Program. Pulmonary function testing (spirometry, methacholine challenges, and exhaled nitric oxide) and body mass index were reviewed. Influenza vaccination rates, oral steroid usage, emergency department visits, and hospitalizations were assessed. Network analysis of the 2 groups also was conducted to evaluate for any associations between the variables.

RESULTS

Cases had significantly higher body mass index percentiles (>75%, odds ratio [OR] 1.64, 95% confidence interval [CI] 1.22-2.2, P = .001). Cases were less likely to have had a methacholine challenge (OR 0.49, 95% CI 0.36-0.68, P < .001) and an exhaled nitric oxide (OR 0.6, 95% CI 0.37-0.97, P = .04) performed than controls. The ratio of forced expiration volume in 1 second to forced vital capacity and forced expiration volume in 1 second were lower in cases than in controls (P < .05). Cases were less likely to have received an influenza vaccination (OR 0.61, 95% CI 0.45-0.82, P = .001) than controls. Unsupervised multivariable network analysis suggested a lack of discrete and unique subgroups between cases and controls.

CONCLUSION

Children with asthma exposed to SHS are more likely to have comorbid conditions such as obesity, more severe asthma, and less health care usage than those not exposed to SHS. Smoking cessation interventions and addressing health disparities could be crucial in this vulnerable population.

Automatic classification of protein structures using low-dimensional structure space mappings

Background

Protein function is closely intertwined with protein structure. Discovery of meaningful structure-function relationships is of utmost importance in protein biochemistry and has led to creation of high-quality, manually curated classification databases, such as the gold-standard SCOP (Structural Classification of Proteins) database. The SCOP database and its counterparts such as CATH provide a detailed and comprehensive description of the structural and evolutionary relationships of the proteins of known structure and are widely employed in structural and computational biology. Since manual classification is both subjective and highly laborious, automated classification of novel structures is increasingly an active area of research. The design of methods for automated structure classification has been rendered even more important since the recent past, due to the explosion in number of solved structures arising out of various structural biology initiatives.

In this paper we propose an approach to the problem of structure classification based on creating and tessellating low dimensional maps of the protein structure space (MPSS). Given a set of protein structures, an MPSS is a low dimensional embedding of structural similarity-based distances between the molecules. In an MPSS, a group of proteins (such as all the proteins in the PDB or sub-samplings thereof) under consideration are represented as point clouds and structural relatedness maps to spatial adjacency of the points. In this paper we present methods and results that show that MPSS can be used to create tessellations of the protein space comparable to the clade systems within SCOP. Though we have used SCOP as the gold standard, the proposed approach is equally applicable for other structural classifications.

Methods

In the proposed approach, we first construct MPSS using pairwise alignment distances obtained from four established structure alignment algorithms (CE, Dali, FATCAT and MATT). The low dimensional embeddings are next computed using an embedding technique called multidimensional scaling (MDS). Next, by using the remotely homologous Superfamily and Fold levels of the hierarchical SCOP database, a distance threshold is determined to relate adjacency in the low dimensional map to functional relationships. In our approach, the optimal threshold is determined as the value that maximizes the total true classification rate vis-a-vis the SCOP classification. We also show that determining such a threshold is often straightforward, once the structural relationships are represented using MPSS.

Results and conclusion

We demonstrate that MPSS constitute highly accurate representations of protein fold space and enable automatic classification of SCOP Superfamily and Fold-level relationships. The results from our automatic classification approach are remarkably similar to those found in the distantly homologous Superfamily level and the quite remotely homologous Fold levels of SCOP. The significance of our results are underlined by the fact that most automated methods developed thus far have only managed to match the closest-homology Family level of the SCOP hierarchy and tend to differ considerably at the Superfamily and Fold levels. Furthermore, our research demonstrates that projection into a low-dimensional space using MDS constitutes a superior noise-reducing transformation of pairwise distances than do the variety of probability- and alignment-length-based transformations currently used by structure alignment algorithms.