Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding

Introduction: Prediction of RNA secondary structure from single sequences still needs substantial improvements. The application of machine learning (ML) to this problem has become increasingly popular. However, ML algorithms are prone to overfitting, limiting the ability to learn more about the inherent mechanisms governing RNA folding. It is natural to use high-capacity models when solving such a difficult task, but poor generalization is expected when too few examples are available.Methods: Here, we report the relation between capacity and performance on a fundamental related problem: determining whether two sequences are fully complementary. Our analysis focused on the impact of model architecture and capacity as well as dataset size and nature on classification accuracy.Results: We observed that low-capacity models are better suited for learning with mislabelled training examples, while large capacities improve the ability to generalize to structurally dissimilar data. It turns out that neural networks struggle to grasp the fundamental concept of base complementarity, especially in lengthwise extrapolation context.Discussion: Given a more complex task like RNA folding, it comes as no surprise that the scarcity of useable examples hurdles the applicability of machine learning techniques to this field

Physiological markers of challenge and threat mediate the effects of performance-based goals on performance

We predicted that adopting a performance-approach vs. performance-avoidance goal would lead to physiological responses characteristic of psychological states of challenge vs. threat appraisals, respectively. Furthermore, we predicted that these states would mediate the effects of goals on performance. Twenty-seven undergraduate females performed a task described as identifying either exceptionally strong performers (performance-approach goal) or exceptionally weak performers (performance-avoidance goal). Participants’ cardiovascular reactivity (CVR) was recorded while they performed the task. As predicted, participants in the performance-approach goal condition performed better on the task than did those in the performance-avoidance goal condition. Also as predicted, those in the former condition exhibited a challenge pattern of CVR whereas those in the latter condition exhibited a threat pattern of CVR. Furthermore, physiological responses mediated the effects of performance-based goals on performance

Physiological markers of challenge and threat mediate the effects of performance-based goals on performance

Manuscript "in press", Journal of Experimental Social PsychologyWe predicted that adopting a performance-approach vs. performance-avoidance goal would lead to physiological responses characteristic of psychological states of challenge vs. threat appraisals, respectively. Furthermore, we predicted that these states would mediate the effects of goals on performance. Twenty-seven undergraduate females performed a task described as identifying either exceptionally strong performers (performance-approach goal) or exceptionally weak performers (performance-avoidance goal). Participants' cardiovascular reactivity (CVR) was recorded while they performed the task. As predicted, participants in the performance-approach goal condition performed better on the task than did those in the performance-avoidance goal condition. Also as predicted, those in the former condition exhibited a challenge pattern of CVR whereas those in the latter condition exhibited a threat pattern of CVR. Furthermore, physiological responses mediated the effects of performance-based goals on performance

Modeling RNA tertiary structure motifs by graph-grammars

A new approach, graph-grammars, to encode RNA tertiary structure patterns is introduced and exemplified with the classical sarcin–ricin motif. The sarcin–ricin motif is found in the stem of the crucial ribosomal loop E (also referred to as the sarcin–ricin loop), which is sensitive to the α-sarcin and ricin toxins. Here, we generate a graph-grammar for the sarcin-ricin motif and apply it to derive putative sequences that would fold in this motif. The biological relevance of the derived sequences is confirmed by a comparison with those found in known sarcin–ricin sites in an alignment of over 800 bacterial 23S ribosomal RNAs. The comparison raised alternative alignments in few sarcin–ricin sites, which were assessed using tertiary structure predictions and 3D modeling. The sarcin–ricin motif graph-grammar was built with indivisible nucleotide interaction cycles that were recently observed in structured RNAs. A comparison of the sequences and 3D structures of each cycle that constitute the sarcin–ricin motif gave us additional insights about RNA sequence–structure relationships. In particular, this analysis revealed the sequence space of an RNA motif depends on a structural context that goes beyond the single base pairing and base-stacking interactions

RKB: a Semantic Web knowledge base for RNA

Increasingly sophisticated knowledge about RNA structure and function requires an inclusive knowledge representation that facilitates the integration of independently –generated information arising from such efforts as genome sequencing projects, microarray analyses, structure determination and RNA SELEX experiments. While RNAML, an XML-based representation, has been proposed as an exchange format for a select subset of information, it lacks domain-specific semantics that are essential for answering questions that require expert knowledge. Here, we describe an RNA knowledge base (RKB) for structure-based knowledge using RDF/OWL Semantic Web technologies. RKB extends a number of ontologies and contains basic terminology for nucleic acid composition along with context/model-specific structural features such as sugar conformations, base pairings and base stackings. RKB (available at http://semanticscience.org/projects/rkb) is populated with PDB entries and MC-Annotate structural annotation. We show queries to the RKB using description logic reasoning, thus opening the door to question answering over independently-published RNA knowledge using Semantic Web technologies

A transcriptome-based approach to identify functional modules within and across primary human immune cells

Genome-wide transcriptomic analyses have provided valuable insight into fundamental biology and disease pathophysiology. Many studies have taken advantage of the correlation in the expression patterns of the transcriptome to infer a potential biologic function of uncharacterized genes, and multiple groups have examined the relationship between co-expression, co-regulation, and gene function on a broader scale. Given the unique characteristics of immune cells circulating in the blood, we were interested in determining whether it was possible to identify functional co-expression modules in human immune cells. Specifically, we sequenced the transcriptome of nine immune cell types from peripheral blood cells of healthy donors and, using a combination of global and targeted analyses of genes within co-expression modules, we were able to determine functions for these modules that were cell lineagespecific or shared among multiple cell lineages. In addition, our analyses identified transcription factors likely important for immune cell lineage commitment and/or maintenance

Fragmento de O Mar e a Selva

Tradução de The Sea and the Jungle, de Henry Major Tomlinson