Kelkar, N. | Search bioRxiv

bioRxiv2019

Environmental influences on acoustic divergence in Rhinolophus bats of the Western Ghats-Sri Lanka region

According to the acoustic adaptation hypothesis, environmental and biogeographic factors such as atmospheric humidity can influence divergence of acoustic signals and speciation in high duty-cycle echolocating bats (e.g. Rhinolophus sp.), although this remains disputed. In this study we tested the hypothesis that Resting Frequency (RF) would decrease with increasing humidity along a large latitudinal gradient (6{degrees}-21{degrees} N), for four Rhinolophus species with different evolutionary histories, in the Western Ghats-Sri Lanka (WGSL) region. We conducted acoustic recordings and compiled published information on RFs of stationary Rhinolophus indorouxi, R. rouxi, R. beddomei, and R. lepidus from 40 roosts in 18 localities of the WGSL. These data comprised of recordings made with different devices and with different settings. Hence, due to the unknown measurement error involved in the recorded RFs, it was not possible to conduct conventional regression analyses to test our hypotheses. Hence, we qualitatively assessed effects of Relative Humidity (RH) and other environmental variables by interpreting only the sign, but not the magnitude of the RF responses (from the slopes of generalized least squares regression models). We also tested how RF and RH varied across biogeographic zones, and with bat body size. RFs of the Miocene-diverged species R. indorouxi and R. rouxi were higher at lower RH, as expected. In contrast, RF of the Pleistocene-diverged species R. beddomei and R. lepidus were higher at higher RH. Elevation and rainfall also emerged as important predictors of RF variation in these species. Bat body size differed in dry and humid regions of the WGSL. RF variation was not consistent across biogeographic zones. The cryptic, phonically differentiated sibling species R. indorouxi and R. rouxi co-occurred only in mid-elevation zones along the Western Ghats escarpment. The variable but significant influences of humidity and correlated factors on RF suggest the importance of environmentally mediated acoustic divergence in different Rhinolophus species in the WGSL. We propose some hypotheses on interacting effects of environmental and phylogenetic factors on acoustic divergence in Rhinolophus bats of the WGSL. These ideas could be further tested with phylogenetic and acoustic studies, as more consistent and comparable data on these species become available in the future.

ecology↗

bioRxiv2018

A machine-learning approach for accurate detection of copy-number variants from exome sequencing

Copy-number variants (CNVs) are a major cause of several genetic disorders, making their detection an essential component of genetic analysis pipelines. Current methods for detecting CNVs from exome sequencing data are limited by high false positive rates and low concordance due to the inherent biases of individual algorithms. To overcome these issues, calls generated by two or more algorithms are often intersected using Venn-diagram approaches to identify \"high-confidence\" CNVs. However, this approach is inadequate, as it misses potentially true calls that do not have consensus from multiple callers. Here, we present CN-Learn, a machine-learning framework (https://github.com/girirajanlab/CN_Learn) that integrates calls from multiple CNV detection algorithms and learns to accurately identify true CNVs using caller-specific and genomic features from a small subset of validated CNVs. Using CNVs predicted by four exome-based CNV callers (CANOES, CODEX, XHMM and CLAMMS) from 503 samples, we demonstrate that CN-Learn identifies true CNVs at higher precision (~90%) and recall (~85%) rates while maintaining robust performance even when trained with minimal data (~30 samples). CN-Learn recovers twice as many CNVs compared to individual callers or Venn diagram-based approaches, with features such as exome capture probe count, caller concordance and GC content providing the most discriminatory power. In fact, about 58% of all true CNVs recovered by CN-Learn were either singletons or calls that lacked support from at least one caller. Our study underscores the limitations of current approaches for CNV identification and provides an effective method that yields high-quality CNVs for application in clinical diagnostics.

genomics↗