Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

Background: Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results: We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion: We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation

MKLP2 Is a Motile Kinesin that Transports the Chromosomal Passenger Complex during Anaphase

During cytokinesis, signals from the anaphase spindle direct the formation and position of a contractile ring at the cell cortex [1]. The chromosomal passenger complex (CPC) participates in cytokinesis initiation by signaling from the spindle midzone and equatorial cortex [2], but the mechanisms underlying the anaphase-specific CPC localization are currently unresolved. Accumulation of the CPC at these sites requires the presence of microtubules and the mitotic kinesin-like protein 2, MKLP2 (KIF20A), a member of the kinesin-6 family [2-7], and this has led to the hypothesis that the CPC is transported along microtubules by MKLP2 [3-5, 7]. However, the structure of the MKLP2 motor domain with its extended neck-linker region suggests that this kinesin might not be able to drive processive transport [8, 9]. Furthermore, experiments in Xenopus egg extracts indicated that the CPC might be transported by kinesin-4, KIF4A [10]. Finally, CPC-MKLP2 complexes might be directly recruited to the equatorial cortex via association with actin and myosin II, independent of kinesin activity [4, 8]. Using microscopy-based assays with purified proteins, we demonstrate that MKLP2 is a processive plus-end directed motor that can transport the CPC along microtubules in vitro. In cells, strong suppression of MKLP2-dependent CPC motility by expression of an MKLP2 P-loop mutant perturbs CPC accumulation at both the spindle midzone and equatorial cortex, whereas a weaker inhibition of MKLP2 motor using Paprotrain mainly affects CPC localization to the equatorial cortex. Our data indicate that control of cytokinesis initiation by the CPC requires its directional MKLP2-dependent transport

MKLP2 Is a Motile Kinesin that Transports the Chromosomal Passenger Complex during Anaphase

During cytokinesis, signals from the anaphase spindle direct the formation and position of a contractile ring at the cell cortex [1]. The chromosomal passenger complex (CPC) participates in cytokinesis initiation by signaling from the spindle midzone and equatorial cortex [2], but the mechanisms underlying the anaphase-specific CPC localization are currently unresolved. Accumulation of the CPC at these sites requires the presence of microtubules and the mitotic kinesin-like protein 2, MKLP2 (KIF20A), a member of the kinesin-6 family [2-7], and this has led to the hypothesis that the CPC is transported along microtubules by MKLP2 [3-5, 7]. However, the structure of the MKLP2 motor domain with its extended neck-linker region suggests that this kinesin might not be able to drive processive transport [8, 9]. Furthermore, experiments in Xenopus egg extracts indicated that the CPC might be transported by kinesin-4, KIF4A [10]. Finally, CPC-MKLP2 complexes might be directly recruited to the equatorial cortex via association with actin and myosin II, independent of kinesin activity [4, 8]. Using microscopy-based assays with purified proteins, we demonstrate that MKLP2 is a processive plus-end directed motor that can transport the CPC along microtubules in vitro. In cells, strong suppression of MKLP2-dependent CPC motility by expression of an MKLP2 P-loop mutant perturbs CPC accumulation at both the spindle midzone and equatorial cortex, whereas a weaker inhibition of MKLP2 motor using Paprotrain mainly affects CPC localization to the equatorial cortex. Our data indicate that control of cytokinesis initiation by the CPC requires its directional MKLP2-dependent transport

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty.

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

How Many Replicators Does It Take to Achieve Reliability? Investigating Researcher Variability in a Crowdsourced Replication

The paper reports findings from a crowdsourced replication. Eighty-four replicator teams attempted to verify results reported in an original study by running the same models with the same data. The replication involved an experimental condition. A “transparent” group received the original study and code, and an “opaque” group received the same underlying study but with only a methods section and description of the regression coefficients without size or significance, and no code. The transparent group mostly verified the original study (95.5%), while the opaque group had less success (89.4%). Qualitative investigation of the replicators’ workflows reveals many causes of non-verification. Two categories of these causes are hypothesized, routine and non-routine. After correcting non-routine errors in the research process to ensure that the results reflect a level of quality that should be present in ‘real-world’ research, the rate of verification was 96.1 in the transparent group and 92.4 in the opaque group. Two conclusions follow: (1) Although high, the verification rate suggests that it would take a minimum of three replicators per study to achieve replication reliability of at least 95 confidence assuming ecological validity in this controlled setting, and (2) like any type of scientific research, replication is prone to errors that derive from routine and undeliberate actions in the research process. The latter suggests that idiosyncratic researcher variability might provide a key to understanding part of the “reliability crisis” in social and behavioral science and is a reminder of the importance of transparent and well documented workflows