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SLAS 2017: Let There Be Light: Informatics Approaches to Exploring the Dark Genome

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I’m organizing a symposium at the 2017 SLAS meeting in Washington D.C in the Data Analysis and Informatics track. The topic of the symposium focuses on informatics approaches that shed light and explore the dark genome. The description is given below, and I hope you’ll consider submitting an abstract.

With efforts such as the NIH-funded Illuminating the Druggable Genome (IDG) program, there is great interest and a pressing need to understand the “dark genome” — the subset of genes that have little to no information about them in the literature or databases. This session will focus on current efforts by members of the IDG program and the community in general on developing informatics resources for data aggregation and integration, target prioritization and platform development. In addition, topics such as characterization of druggability and novel approaches to connecting heterogeneous datasets that allow us to shed light on the dark genome will be considered.

The deadline is Aug 8, 2016 and you can submit an abstract here.

Written by Rajarshi Guha

May 11th, 2016 at 3:46 pm

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Analysing Differential Activity in Dose Response Screens

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My colleagues and I recently published a paper where we explored a few methods to identify differential behavior in dose response screens. While there is an extensive literature about analyzing differential effects in genomic data (e.g. mciroarrays, RNAseq), these methods are based on distributional assumptions that holds for genomic data. This is not necessarily the case for small molecule, dose response data. A separate post will explore this aspect.

So we couldn’t directly apply the methods devised for genomic data. Another issue that we wanted to address was the lack of replicates. As a result certain methods are excluded from consideration (e.g., t-test based methods). The simplest case (or what we refer to as obviously differential) is when a compound is active in one treatment and completely inactive in the other. This is trivial to characterize. The next method we considered was to look at fold changes for individual curve fit parameters and then choose an arbitrary threshold. This is not a particularly robust approach, and has no real statistical basis. However, such thresholding is still used in a number of scenarios (e.g., cherry picking in single point screens). In addition, in this approach you have to choose one of many parameters. So finally, we considered a data fusion approach, that ranked compounds using the rank product method. This method employed potency, response at the highest concentration and the AUC. The nice thing about this method is that it doesn’t require choosing a threshold, provides an empirical p-value and is flexible enough to include other relevant parameters (say, physicochemical properties).

Finally, we examined how single point data (modeled using the response at the highest concentration) compared to dose response data at identifying differential actives. As one might expect, the obviously differential compounds were easily identified. However for compounds active in both treatments, the single point approach led to more false positives. Thus, even those dose response is more resource-intensive, the improved accuracy makes it worth it.

In the next post I’ll look at some of the issues that didn’t make in to this paper – in particular hypothesis based tests that focus on testing differences between model fits. One key observation (also suggested by Gelman) is that strict p-value cutoffs lead one to focus on obvious or well-known effects. For small-scale exploratory analyses such as described in this paper, a more relaxed threshold of 0.1 might be more suitable, allowing marginal effects that may, however, be biologically interesting to be considered.

Written by Rajarshi Guha

May 2nd, 2016 at 2:10 am

Call For Papers: Shedding Light on the Dark Genome – Methods, Tools & Case Studies

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252nd ACS National Meeting
Philadelphia, Aug 21-25, 2016
CINF Division

Dear Colleagues, we are organizing a symposium at the Fall ACS meeting in Philadelphia focusing on computational, experimental and hybrid approaches to characterizing the unstudied and understudied druggable genome.  In 2014 the NIH initiated a program titled, “Illuminating the Druggable Genome” (IDG) with the goal of improving our understanding of the properties and functions of proteins that are currently unannotated within the four most commonly drug-targeted protein families – GPCRs, ion channels, nuclear receptors and kinases. As part of this program a Knowledge Management Center (KMC) was formed, as a collaboration between six academic center, who’s goal was to develop an integrative informatics platform to collect data, develop data driven prioritization schemes, analytical methods  and disseminate standardized/annotated information related to the unannotated proteins in the four gene families of interest.

In this symposium, members of the various components of the IDG program will present the results of ongoing work related to experimental methods, target prioritization, data aggregation and platform development. In addition, we welcome contributions related to the identification of druggable targets, approaches to quantify druggability and novel approaches to integrating disparate data source with the goal of shedding light on the “dark genome”

The deadline for abstract submissions is March 29, 2016. All abstracts should be submitted via MAPS at If you have any questions feel free to contact  Tudor or myself

Rajarshi Guha

Tudor Oprea
University of New Mexico

Written by Rajarshi Guha

February 22nd, 2016 at 4:00 pm

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vSDC, Rank Products and DUD-E

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This post is a follow-up to my previous discussion on a paper by Chaput et al. The gist of that paper was that in a virtual screening scenario where a small number of hits are to be selected for followup, one could use an ensemble of docking methods, identify compounds whose scores were beyond 2SD of the mean for each method and take the intersection. My post suggested that a non-parametric approach (rank products, RP) performed similarly to the parametric approach of Chaput et al on the two targets they screened.

The authors also performed a benchmark comparison of their consensus method (vSDC) versus the individual docking methods for 102 DUD-E targets. I was able to obtain the individual docking scores (Glide, Surflex, FlexX and GOLD) for each of the targets, with the aim of applying the rank product method described previously.

In short, I reproduced Figure 6A (excluding the curve for vSDC). In
th0this figure, \(n_{test}\) is the number of compounds selected (from the ranked list, either by individual docking scores or by the rank product) and \(T_{h>0}\) is the percentage of targets for which the \(n_{test}\) selected compounds included one or more actives. Code is available here, but you’ll need to get in touch with the authors for the DUD-E docking scores.

As shown alongside, the RP method (as expected) outperforms the individual docking methods. And visual comparison with the original figure suggests that it also outperforms vSDC, especially at lower values of \(n_{test}\). While I wouldn’t regard the better performance of RP compared to vSDC as a huge jump, the absence of a threshold certainly works in its favor.

One could certainly explore ranking approaches in more depth. As suggested by Abhik Seal, Borda or Condorcet methods could be examined (though the small number of docking methods, a.k.a., voter, could be problematic).

UPDATE: After a clarification from Liliane Mouawad it turns out there was a mistake in the ranking of the Surflex docking scores. Correcting that bug fixes my reproduction of Figure 6A so that the curves for individual docking methods match the original. But more interestingly, the performance of RP is now clearly better than every individual method and the vSDC method as well, at all values of \(n_{test}\)

Written by Rajarshi Guha

February 13th, 2016 at 7:25 pm

Hit Selection When You’re Strapped for Cash

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I came across a paper from Chaput et al that describes an approach to hit selection from a virtual screen (using docking), when follow-up resources are limited (a common scenario in many academic labs). Their approach is based on using multiple docking programs. As they (and others) have pointed out, there is a wide divergence between the rankings of compounds generated using different programs. Hence the motivation for a consensus approach, based on the estimating the standard deviation (SD) of scores generated by a given program and computing the intersection of compounds whose scores are greater than 2 standard deviations from the mean, in each program. Based on this rule, they selected relatively few compounds – just 14 to 22, depending on the target and confirmed at least one of them for each target. This represents less than 0.5% of their screening deck.

However, their method is parametric – you need to select a SD threshold. I was interested in seeing whether a non-parametric, ranking based approach would allow one to retrieve a subset that included the actives identified by the authors. The method is essentially the rank product method applied to the docking scores. That is, the compounds are ranked based on their docking scores and the “ensemble rank” for a compound is the product of its ranks according to each of the four programs. In contrast to the original definition, I used a sum log rank to avoid overflow issues. So the ensemble rank for the \(i\)’th compound is given by

\(R_i = \sum_{j=1}^{4} \log r_{ij}\)

where \(r_{ij}\) is the rank of the \(i\)’th compound in the \(j\)’th docking program. Compounds are then selected based on their ensemble rank. Obviously this doesn’t give you a selection per se. Instead, this allows you to select as many compounds as you want or need. Importantly, it allows you to introduce external factors (cost, synthetic feasibility, ADME properties, etc.) as additional rankings that can be included in the ensemble rank.

Using the docking scores for Calcineurin and Histone Binding Protein (Hbp) provided by Liliane Mouawad (though all the data really should’ve been included in the paper) I applied this method using the code below

d <- read.table('',
                header=TRUE, comment='')
names(d) <- c('molid', 'Surflex', 'Glide', 'Flexx', 'GOLD')
d$GOLD <- -1*d$GOLD ## Since higher scores are better
ranks <- apply(d[,-1], 2, rank)
lranks <- rowSums(log(ranks))
tmp <- data.frame(molid=d[,1], ranks, lrp=rp)
tmp <- tmp[order(tmp$lrp),]
which(str_detect(tmp$molid, 'ACTIVE'))

and identified the single active for Hbp at ensemble rank 8 and the three actives for Calcineurin at ranks 3, 5 and 25. Of course, if you were selecting only the top 3 you would’ve missed the Calcineurin hit and only have gotten 1/3 of the HBP hits. However, as the authors nicely showed, manual inspection of the binding poses is crucial to making an informed selection. The ranking is just a starting point.

Update: Docking scores for Calcineurin and Hbp are now available

Written by Rajarshi Guha

February 5th, 2016 at 1:36 am