preprint reviews by John Didion

Functional and non-functional classes of peptides produced by long non-coding RNAs

Sourced from http://biorxiv.org/content/early/2016/10/24/064915.

Jorge Ruiz-Orera, Pol Verdaguer-Grau, José Luis Villanueva-Cañas, Xavier Messeguer, M Mar Albà

Review posted on 03rd January 2017

We reviewed this paper in our December preprint journal club. Overall, we found the paper to be well written and the conclusions to be convincing. We had only a few minor comments and suggestions:<...

See full review here.


Total RNA Sequencing reveals microbial communities in human blood and disease specific effects

Sourced from http://biorxiv.org/content/early/2016/06/07/057570.

Serghei Mangul, Loes M Olde Loohuis, Anil Ori, Guillaume Jospin, David Koslicki, Harry Taegyun Yang, Timothy Wu, Marco P Boks, Catherine Lomen-Hoerth, Martina Wiedau-Pazos, Rita Cantor, Willem M de Vos, Rene S Kahn, Eleazar Eskin, Roel A. Ophoff

Review posted on 03rd August 2016

We reviewed this paper in our July preprint journal club. Obviously, the potential for contamination to influence the results is the first question that all reviewers will ask. Although we were imp...

See full review here.


Different Evolutionary Paths to Complexity for Small and Large Populations of Digital Organisms

Sourced from http://biorxiv.org/content/early/2016/04/22/049767.

Thomas LaBar, Christoph Adami

Review posted on 14th June 2016

We reviewed this paper in our May preprint journal club.

This is a clever use of Avida to look at the dynamics of genome evolution.

We debated the choice of 15 essential instructions as...

See full review here.


Many long intergenic non-coding RNAs distally regulate mRNA gene expression levels

Sourced from http://biorxiv.org/content/early/2016/03/19/044719.

Ian McDowell, Athma Pai, Cong Guo, Christopher M Vockley, Christopher D Brown, Timothy E Reddy, Barbara E Engelhardt

Review posted on 15th April 2016

We reviewed this paper in our April preprint journal club at NHGRI. Overall, we enjoyed the paper and it fostered good discussion. An interesting point you could bring up in the intro or discussion...

See full review here.


Methylation Analysis Reveals Fundamental Differences Between Ethnicity and Genetic Ancestry

Joshua M Galanter, Christopher R Gignoux, Sam S Oh, Dara Torgerson, Maria Pino-Yanes, Neeta Thakur, Celeste Eng, Donglei Hu, Scott Huntsmann, Harold J Farber, Pedro Avila, Emerita Brigino-Buenaventura, Michael LeNoir, Kelly Meade, Denise Serebrisky, William Rodriguez-Cintron, Raj Kumar, Jose R Rodriguez-Santana, Max Seibold, Luisa Borrell, Esteban G Burchard, Noah Zaitlen

Review posted on 10th March 2016

We reviewed this paper in our February 2016 preprint journal club. First, we found the research question interesting and important – if a substantial fraction of ethnicity is explained by non-genetic effects, then this is clinically relevant information and should be taken into account during treatment, drug development and testing, etc. Our main concern was that the study design makes it difficult to believe that any associations with Puerto Rican ancestry are not due to environmental effects, since nearly 90% of the self-identified Puerto Ricans and none of the self-identified Mexicans were recruited in Puerto Rico. The authors seem to realize this problem because at several points they either test for association with recruitment site, or correct for recruitment site in tests of association between ethnicity and methylation. However, we suspect that, if instead of using recruitment site as a multi-value or continuous covariate the authors use “recruitment site == Puerto Rico” as a binary covariate, some of the significant associations between methylation and ethnicity might go away. If we were reviewers on this paper, we would ask for that additional analysis. Similarly, trying to identify methylation effects of Puerto Rican ethnicity that are independent of environmental differences that are particular to Puerto Rico (perhaps there’s a different smoking rate or level of air pollution there than in the other recruitment sites?) is problematic given this study’s data set.

Another analysis that we think is important when comparing results to previously reported findings is testing whether the effect sizes and directions are consistent. For example, in the “Ethnic differences in environmentally-associated methylation sites” section, do the 19 nominally significant loci that were previously identified in a study of Norwegian newborns have the same direction of methylation change between the two studies? This would require you to know the smoking rate among your sample populations, but you could use the population smoking rates at the recruitment sites as a reasonable proxy.
Some minor comments:
• The cis-meQTL analysis is certainly important, but it would be nice to know whether you tested for trans effects, and whether any loci came up significant.
• We found it a bit odd that Bonferroni correction was used rather than the now more common FDR control. Does the number of significant associations change when using FDR <= 0.05 rather than a p-value threshold?
• For figures 1-3, the A panels are genome-wide analyses while the remainder of the panels focus on a specific locus. The A panels should either be split into separate figures, or each panel should be very clearly labeled with a title indicating what is being shown.

show less


A community overlap strategy reveals central genes and networks in heart failure

Sourced from http://biorxiv.org/content/early/2016/01/28/038174.

Pablo Cordero, Ayca Erbilgin, Ching Shang, Michael P Morley, Mathew Wheeler, Frederick Dewey, Kevin S Smith, Ray Hu, Jeffrey Brandimarto, Yichuan Liu, Mingyao Li, Hongzhe Li, Scott Ritter, Sihai H Zhao, Komal S Rathi, Liming Qu, Avinash Das, Stephen Montgomery, Sridhar Hannenhalli, Christine S Moravec, Wilson H Tang, Kenneth B Margulies, Thomas P Cappola, Euan A Ashley

Review posted on 10th March 2016

We discussed this paper in our preprint journal club on 3/3/16. Our comments:

We appreciate that the authors corrected for demographic covariates and batch effects. However, there are additio...

See full review here.


Disease variants alter transcription factor levels and methylation of their binding sites

Marc Jan Bonder, René Luijk, Daria Zhernakova, Matthijs Moed, Patrick Deelen, Martijn Vermaat, Maarten van Iterson, Freerk van Dijk, Michiel van Galen, Jan Bot, Roderick C. Slieker, P. Mila Jhamai, Michael Verbiest, H. Eka D. Suchiman, Marijn Verkerk, Ruud van der Breggen, Jeroen van Rooij, Nico Lakenberg, Wibowo Arindrarto, Szymon M. Kielbasa, Iris Jonkers, Peter van t Hof, Irene Nooren, Marian Beekman, Joris Deelen, Diana van Heemst, Alexandra Zhernakova, Ettje F. Tigchelaar, Morris A. Swertz, Albert Hofman, André G. Uitterlinden, René Pool, Jenny van Dongen, Jouke J. Hottenga, Coen D.A. Stehouwer, Carla J.H. van der Kallen, Casper G. Schalkwijk, Leonard H. van den Berg, Erik. W van Zwet, Hailiang Mei, Mathieu Lemire, Thomas J. Hudson, P. Eline Slagboom, Cisca Wijmenga, Jan H. Veldink, Marleen M.J. van Greevenbroek, Cornelia M. van Duijn, Dorret I. Boomsma, Aaron Isaacs, Rick Jansen, Joyce van Meurs, Peter A.C. t Hoen, Lude Franke, Bastiaan T. Heijmans

Review posted on 11th January 2016

We reviewed this paper in our preprint-focused journal club at NIH/NHGRI. Generally, we were very impressed with the depth of the data set, and with the care take in choice of analytical approaches.


We recommend adding a section to the introduction explaining the different models that might explain relationships between SNPs, gene expression, and methylation, and which (if any) the authors, at the outset of their study, hypothesized to explain all (or the majority) of associations. For example, was TF binding expected to cause changes in methylation within/near the binding site (and if so, how), and/or was methylation expected to disrupt TF binding?

One substantial concern we had was with the use of language that implies causality. The authors found significant correlations in their eQTL, meQTL, and eQTM analyses that allow for testable hypotheses and working models to be generated, but the lack of any functional validation means that causality cannot be determined. For one example, the word “affects” on line 158 should be replaced with “is associated with.”

One obvious analysis we expected to see was the association between eQTL of genes that encode methyltransferases and methyl-binding proteins, and the targets of those proteins (or global methylation levels, in the case of non-specific methyltransferases). If such an association was looked for and not found, the authors should say so (in the supplement, at the very least). Other associations the authors could probe for, but which may be outside the scope of the paper, are non-coding RNAs (especially in light of the findings in Lemire et al) and small RNAs.

Minor comments:
• The figure legends need to be more informative, especially for figure 1. It was very difficult to understand what was going on in the panel below 1A/B.
• The pie chart in figure 1D is difficult to interpret. Please consider a bar chart instead.
• You never define meQTL. It would be especially helpful to have a sentence distinguishing between usage that implies a particular SNP (which may be associated with multiple CpGs) versus individual SNP-CpG pairs.
• In figure 4a, methylation levels are shown relative to the minor allele for each SNP. However, in the text, alleles are referred to as risk or non-risk, but it is never stated whether the risk allele is the minor allele. We suggest modifying the figure to instead display values in terms of risk alleles.
• It would be helpful to mention how much of the genome is being interrogated by the current method. The authors may be able to speculate, or to predict from whole-genome bisulfite sequencing data generated in other studies, how much they are missing by using only sites probed by the 450k array.

show less