Biology subjects

Gang Chen

Publications and source records attributed to Gang Chen.

3 recordsLinked to original sources

bioRxiv2016

Why are frameshift homologs widespread within and across species?

Frameshift protein sequences encoded by alternative reading frames of coding genes have been considered meaningless, and frameshift mutations have been considered of little importance for the molecular evolution of coding genes and proteins. However, functional frameshifts have been found widely existing. It was puzzling how a frameshift protein kept its structure and functionality while its amino-acid sequence was changed substantially. Here we show that frame similarities between frameshifts and wild types are higher than random similarities and are defined at the genetic code, gene, and genome levels. In the standard genetic code, frameshift codon substitutions are more conservative than random substitutions. The frameshift tolerability of the standard genetic code ranks in the top 2.0-3.5% of alternative genetic codes, showing that the genetic code is nearly optimal for frameshift tolerance. Furthermore, frameshift-resistant codons (codon pairs) appear more frequently than expected in many genes and certain genomes, showing that the frameshift optimality is reflected not only in the genetic code but more importantly, in its allowance of further optimizing the frameshift tolerance of a particular gene or genome, which shed light on the role of frameshift mutations in molecular and genomic evolution.

Genetics↗

bioRxiv2016

Is the Statistic Value All We Should Care about in Neuroimaging?

Here we address an important issue that has been embedded within the neuroimaging community for a long time: the absence of effect estimates in results reporting in the literature. The statistic value itself, as a dimensionless measure, does not provide information on the biophysical interpretation of a study, and it certainly does not represent the whole picture of a study. Unfortunately, in contrast to standard practice in most scientific fields, effect (or amplitude) estimates are usually not provided in most results reporting in the current neuroimaging publications and presentations. Possible reasons underlying this general trend include: 1) lack of general awareness, 2) software limitations, 3) inaccurate estimation of the BOLD response, and 4) poor modeling due to our relatively limited understanding of FMRI signal components. However, as we discuss here, such reporting damages the reliability and interpretability of the scientific findings themselves, and there is in fact no overwhelming reason for such a practice to persist. In order to promote meaningful interpretation, cross validation, reproducibility, meta and power analyses in neuroimaging, we strongly suggest that, as part of good scientific practice, effect estimates should be reported together with their corresponding statistic values. We provide several easily adaptable recommendations for facilitating this process.

Neuroscience↗

bioRxiv2016

Sharing brain mapping statistical results with the neuroimaging data model

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html.

Neuroscience↗