Different second-generation sequencing technologies may have taxon-specific biases when DNA metabarcoding prey in predator faeces. Our major objective was to examine differences in prey recovery from bat guano across two different sequencing workflows using the same faecal DNA extracts. We compared results between the Ion Torrent PGM and the Illumina MiSeq with similar library preparations and the same analysis pipeline. We focus on repeatability and provide an R Notebook in an effort towards transparency for future methodological improvements. Full documentation of each step enhances the accessibility of our analysis pipeline. We tagged DNA from insectivorous bat faecal samples, targeted the arthropod cytochrome c oxidase I minibarcode region and sequenced the product on both second-generation sequencing platforms. We developed an analysis pipeline with a high operational taxonomic unit (OTU) clustering threshold (i.e., ≥98.5%) followed by copy number filtering to avoid merging rare but genetically similar prey into the same OTUs. With this workflow, we detected 297 unique prey taxa, of which 74% were identified at the species level. Of these, 104 (35%) prey OTUs were detected by both platforms, 176 (59%) OTUs were detected by the Illumina MiSeq system only, and 17 (6%) OTUs were detected using the Ion Torrent system only. Costs were similar between platforms but the Illumina MiSeq recovered six times more reads and four additional insect orders than did Ion Torrent. The considerations we outline are particularly important for long-term ecological monitoring; a more standardized approach will facilitate comparisons between studies and allow faster recognition of changes within ecological communities.
The area of derandomization attempts to provide efficient deterministic simulations of randomized algorithms in various algorithmic settings. Goldreich and Wigderson introduced a notion of "typically-correct" deterministic simulations, which are allowed to err on few inputs. In this paper, we further the study of typically-correct derandomization in two ways. First, we develop a generic approach for constructing typically-correct derandomizations based on seed-extending pseudorandom generators, which are pseudorandom generators that reveal their seed. We use our approach to obtain both conditional and unconditional typicallycorrect derandomization results in various algorithmic settings. We show that our technique strictly generalizes an earlier approach by Shaltiel based on randomness extractors and simplifies the proofs of some known results. We also demonstrate that our approach is applicable in algorithmic settings where earlier work did not apply. For example, we present a typically-correct polynomial-time simulation for every language in BPP based on a hardness assumption that is (seemingly) weaker than the ones used in earlier work. Second, we investigate whether typically-correct derandomization of BPP implies circuit lower bounds. Extending the work of Kabanets and Impagliazzo for the zero-error case, we establish a positive answer for error rates in the range considered by Goldreich and Wigderson. In doing so, we provide a simpler proof of the zero-error result. Our proof scales better than the original one and does not rely on the result by 4 Kinne et al.cc 21 (2012) Impagliazzo, Kabanets, and Wigderson that NEXP having polynomialsize circuits implies that NEXP coincides with EXP.
Abstract. The area of derandomization attempts to provide efficient deterministic simulations of randomized algorithms in various algorithmic settings. Goldreich and Wigderson introduced a notion of "typicallycorrect" deterministic simulations, which are allowed to err on few inputs. In this paper we further the study of typically-correct derandomization in two ways. First, we develop a generic approach for constructing typically-correct derandomizations based on seed-extending pseudorandom generators, which are pseudorandom generators that reveal their seed. We use our approach to obtain both conditional and unconditional typically-correct derandomization results in various algorithmic settings. We show that our technique strictly generalizes an earlier approach by Shaltiel based on randomness extractors, and simplifies the proofs of some known results. We also demonstrate that our approach is applicable in algorithmic settings where earlier work did not apply. For example, we present a typically-correct polynomial-time simulation for every language in BPP based on a hardness assumption that is weaker than the ones used in earlier work. Second, we investigate whether typically-correct derandomization of BPP implies circuit lower bounds. Extending the work of Kabanets and Impagliazzo for the zero-error case, we establish a positive answer for error rates in the range considered by Goldreich and Wigderson. In doing so, we provide a simpler proof of the zero-error result. Our proof scales better than the original one and does not rely on the result by Impagliazzo, Kabanets, and Wigderson that NEXP having polynomial-size circuits implies that NEXP coincides with EXP.
We prove space hierarchy and separation results for randomized and other semantic models of computation with advice where a machine is only required to behave appropriately when given the correct advice sequence. Previous works on hierarchy and separation theorems for such models focused on time as the resource. We obtain tighter results with space as the resource. Our main theorems deal with space-bounded randomized machines that always halt. Let s(n) be any space-constructible monotone function that is Ω(log n) and let s ′ (n) be any function such that s ′ (n) = ω(s(n + as(n))) for all constants a.There exists a language computable by two-sided error randomized machines using s ′ (n) space and one bit of advice that is not computable by two-sided error randomized machines using s(n) space and min(s(n), n) bits of advice. There exists a language computable by zero-sided error randomized machines in space s ′ (n) with one bit of advice that is not computable by one-sided error randomized machines using s(n) space and min(s(n), n) bits of advice.If, in addition, s(n) = O(n) then the condition on s ′ above can be relaxed to s ′ (n) = ω(s(n+1)). This yields tight space hierarchies for typical space bounds s(n) that are at most linear. We also obtain results that apply to generic semantic models of computation.
BackgroundMost existing tools for detecting next-generation sequencing-based splicing events focus on generic splicing events. Consequently, special types of non-canonical splicing events of short mRNA regions (IRE1α targeted) have not yet been thoroughly addressed at a genome-wide level using bioinformatics approaches in conjunction with next-generation technologies. During endoplasmic reticulum (ER) stress, the gene encoding the RNase Ire1α is known to splice out a short 26 nt region from the mRNA of the transcription factor Xbp1 non-canonically within the cytosol. This causes an open reading frame-shift that induces expression of many downstream genes in reaction to ER stress as part of the unfolded protein response (UPR). We previously published an algorithm termed “Read-Split-Walk” (RSW) to identify non-canonical splicing regions using RNA-Seq data and applied it to ER stress-induced Ire1α heterozygote and knockout mouse embryonic fibroblast cell lines. In this study, we have developed an improved algorithm “Read-Split-Run” (RSR) for detecting genome-wide Ire1α-targeted genes with non-canonical spliced regions at a faster speed. We applied the RSR algorithm using different combinations of several parameters to the previously RSW tested mouse embryonic fibroblast cells (MEF) and the human Encyclopedia of DNA Elements (ENCODE) RNA-Seq data. We also compared the performance of RSR with two other alternative splicing events identification tools (TopHat (Trapnell et al., Bioinformatics 25:1105–1111, 2009) and Alt Event Finder (Zhou et al., BMC Genomics 13:S10, 2012)) utilizing the context of the spliced Xbp1 mRNA as a positive control in the data sets we identified it to be the top cleavage target present in Ire1α+/− but absent in Ire1α−/− MEF samples and this comparison was also extended to human ENCODE RNA-Seq data.ResultsProof of principle came in our results by the fact that the 26 nt non-conventional splice site in Xbp1 was detected as the top hit by our new RSR algorithm in heterozygote (Het) samples from both Thapsigargin (Tg) and Dithiothreitol (Dtt) treated experiments but absent in the negative control Ire1α knock-out (KO) samples. Applying different combinations of parameters to the mouse MEF RNA-Seq data, we suggest a General Linear Model (GLM) for both Tg and Dtt treated experiments. We also ran RSR for a human ENCODE RNA-Seq dataset and identified 32,597 spliced regions for regular chromosomes. TopHat (Trapnell et al., Bioinformatics 25:1105–1111, 2009) and Alt Event Finder (Zhou et al., BMC Genomics 13:S10, 2012) identified 237,155 spliced junctions and 9,129 exon skipping events (excluding chr14), respectively. Our Read-Split-Run algorithm also outperformed others in the context of ranking Xbp1 gene as the top cleavage target present in Ire1α+/− but absent in Ire1α−/− MEF samples. The RSR package including source codes is available at http://bioinf1.indstate.edu/RSR and its pipeline source codes are also freely available at https://github.com/xuric/read-split-run for academic use.ConclusionsOur ne...
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