Top 10 Biorxiv Papers Today in Bioinformatics


2.033 Mikeys
#1. Managing genomic variant calling workflows with Swift/T
Azza Ahmed, Jacob Heldenbrand, Yan Asmann, Faisal Fadlelmola, Daniel Katz, Katherine Kendig, Matthew Kendzior, Tiffany Li, Yingxue Ren, Elliott Rodriguez, Matthew Weber, Jennie Zermeno, Justin Wozniak, Liudmila Sergeevna Mainzer
Genomic variant discovery is frequently performed using the GATK Best Practices variant calling pipeline, a complex workflow with multiple steps, fans/merges, and conditionals. This complexity makes management of the workflow difficult on a computer cluster, especially when running in parallel on large batches of data: hundreds or thousands of samples at a time. Here we describe a wrapper for the GATK-based variant calling workflow using the Swift/T parallel scripting language. Standard built-in features include the flexibility to split by chromosome before variant calling, optionally permitting the analysis to continue when faulty samples are detected, and allowing users to analyze multiple samples in parallel within each cluster node. The use of Swift/T conveys two key advantages: (1) Thanks to the embedded ability of Swift/T to transparently operate in multiple cluster scheduling environments (PBS Torque, SLURM, Cray aprun environment, etc.,) a single workflow is trivially portable across numerous clusters; (2) The leaf...
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biorxivpreprint: Managing genomic variant calling workflows with Swift/T https://t.co/PyQZMZ8RFN #bioRxiv
biorxiv_bioinfo: Managing genomic variant calling workflows with Swift/T https://t.co/sutP7skG8U #biorxiv_bioinfo
razoralign: Managing genomic variant calling workflows with Swift/T: https://t.co/ipNgxhVEQY https://t.co/TEwPa9VaMi
MrsLaviniaG: RT @biorxiv_bioinfo: Managing genomic variant calling workflows with Swift/T https://t.co/sutP7skG8U #biorxiv_bioinfo
francois_sabot: RT @biorxiv_bioinfo: Managing genomic variant calling workflows with Swift/T https://t.co/sutP7skG8U #biorxiv_bioinfo
ChongjingX: RT @biorxiv_bioinfo: Managing genomic variant calling workflows with Swift/T https://t.co/sutP7skG8U #biorxiv_bioinfo
Github
Repository: Swift-T-Variant-Calling
User: ncsa
Language: Swift
Stargazers: 2
Subscribers: 6
Forks: 1
Open Issues: 6
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Sample Sizes : None.
Authors: 14
Total Words: 10258
Unqiue Words: 3918

2.02 Mikeys
#2. phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes
Harald R Gruber-Vodicka, Brandon KB Seah, Elmar Pruesse
The SSU rRNA gene is the key marker in molecular ecology for all domains of life, but is largely absent from metagenome-assembled genomes that often are the only resource available for environmental microbes. Here we present phyloFlash, a pipeline to overcome this gap with rapid, SSU rRNA-centered taxonomic classification, targeted assembly, and graph-based binning of full metagenomic assemblies. We show that a cleanup of artifacts is pivotal even with a curated reference database. With such a filtered database, the general-purpose mapper BBmap extracts SSU rRNA reads five times faster than the rRNA-specialized tool SortMeRNA with similar sensitivity and higher selectivity on simulated metagenomes. Reference-based targeted assemblers yielded either highly fragmented assemblies or high levels of chimerism, so we employ the general-purpose genomic assembler SPAdes. Our optimized implementation is independent of reference database composition and has satisfactory levels of chimera formation. Using the phyloFlash workflow we could...
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Chemosym: Works in a flash! Fantastic pipeline for extremely rapid recovery of 16S rRNA sequences from metagenomic data: phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/absyu6Xo7W
Yui_Sato1: Now on bioRxiv preprint is phyloFlash: our most-used in-house pipeline! Gives you a nice grasp of your sequence data by 16S18S profiling lighting fast and much more. Very useful. Well done, @GruberVodicka , Brandon, Elmar! #metagenome #bioinformatics #NGS https://t.co/yieJ7koZ1a
benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
AnnaMankowski: Wanna know who's hiding in your metagenomes? PhyloFlash gives you an overview of the 16S/18S sequences present in your sequencing data ⚡️ https://t.co/5Y3wRuziWS
jcamthrash: phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/FnIC6bCeZQ
jstkiefer: sounds good. will test it on monday! phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/GWKc6u169e
PromPreprint: phyloFlash -- Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/sPDf0nTjvB
brochlorococcus: phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/LOfXmre78Z
sio_stef: phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/R8RvfsRsT4
BioRxivCurator: phyloFlash -- Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/SUmLb9nEWi
msmjetten: RT @jcamthrash: phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/FnIC6bCeZQ
scientificjules: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
peterse47663885: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
wildthang1276: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
ppjevac: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
VDelafont: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
AnitaBollmann: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
MaximRubinBlum: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
DerekSeveri: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
seaweedomics: RT @brochlorococcus: phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/LOfXmre78Z
CarloBerg_CB: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
km_tsui: RT @brochlorococcus: phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/LOfXmre78Z
maximilian_frnk: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
termofilos: RT @brochlorococcus: phyloFlash — Rapid SSU rRNA profiling and targeted assembly from metagenomes https://t.co/LOfXmre78Z
PhilippProts: RT @benebio3D: Everybody from the @MarineMicrobio is using it already, now it is also citeable: ⚡️⚡️phyloFlash⚡️⚡️ https://t.co/XKesgZO2Yj
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Sample Sizes : [4]
Authors: 3
Total Words: 10911
Unqiue Words: 3034

2.012 Mikeys
#3. On the Complexity of Sequence to Graph Alignment
Chirag Jain, Haowen Zhang, Yu Gao, Srinivas Aluru
Availability of extensive genetics data across multiple individuals and populations is driving the growing importance of graph based reference representations. Aligning sequences to graphs is a fundamental operation on several types of sequence graphs (variation graphs, assembly graphs, pan-genomes, etc.) and their biological applications. Though research on sequence to graph alignments is nascent, it can draw from related work on pattern matching in hypertext. In this paper, we study sequence to graph alignment problems under Hamming and edit distance models, and linear and affine gap penalty functions, for multiple variants of the problem that allow changes in query alone, graph alone, or in both. We prove that when changes are permitted in graphs either standalone or in conjunction with changes in the query, the sequence to graph alignment problem is NP-complete under both Hamming and edit distance models for alphabets of size ≥ 2. For the case where only changes to the sequence are permitted, we present an O(|V|+m|E|) time...
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razoralign: On the Complexity of Sequence to Graph Alignment: https://t.co/dSbeN0AkDz https://t.co/UNHtCGhawi
chirgjain: Learn about the difficulty and new ways to align sequences to graphs in https://t.co/SaTwsUDN7W; work w/ @haowen_zhang, Yu Gao and Srinivas Aluru (to appear in #RECOMB19)
jsantoyo: On the Complexity of Sequence to Graph Alignment. https://t.co/NKjyWNOw4P
EdinburghWatch: RT @jsantoyo: On the Complexity of Sequence to Graph Alignment. https://t.co/NKjyWNOw4P
ZaminIqbal: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
maxal6: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
gaby_wald: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
mehrshmali: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
boti_ka: RT @biorxivpreprint: On the Complexity of Sequence to Graph Alignment https://t.co/PL4bLrhAup #bioRxiv
francois_sabot: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
pierre_marijon: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
kundu_ritu: RT @chirgjain: Learn about the difficulty and new ways to align sequences to graphs in https://t.co/SaTwsUDN7W; work w/ @haowen_zhang, Yu G…
rzolau: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
vallenet: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
GUILLAUMEGAUTRE: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
jspathmanathan: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
BeNoel7: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
DrStagiaire: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
hdeshmuk: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
haowen_zhang: RT @biorxiv_bioinfo: On the Complexity of Sequence to Graph Alignment https://t.co/gTxVkr0jkR #biorxiv_bioinfo
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Authors: 4
Total Words: 7468
Unqiue Words: 1894

2.008 Mikeys
#4. VPAC: Variational projection for accurate clustering of single-cell transcriptomic data
Shengquan Chen, Kui Hua, Hongfei Cui, Rui Jiang
Background: Single-cell RNA-sequencing (scRNA-seq) technologies have advanced rapidly in recent years and enabled the quantitative characterization at a microscopic resolution. With the exponential growth of the number of cells profiled in individual scRNA-seq experiments, the demand for identifying putative cell types from the data has become a great challenge that appeals for novel computational methods. Although a variety of algorithms have recently been proposed for single-cell clustering, such limitations as low accuracy, inferior robustness, and inadequate stability greatly impede the scope of applications of these methods. Results: We propose a novel model-based algorithm, named VPAC, for accurate clustering of single-cell transcriptomic data through variational projection, which assumes that single-cell samples follow a Gaussian mixture distribution in a latent space. Through comprehensive validation experiments, we demonstrate that VPAC can not only be applied to datasets of discrete counts and normalized continuous data,...
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razoralign: VPAC: Variational projection for accurate clustering of single-cell transcriptomic data: https://t.co/gB7gKOiQe4
claczny: RT @biorxiv_bioinfo: VPAC: Variational projection for accurate clustering of single-cell transcriptomic data https://t.co/OBm5zsdk3A #bior…
IlottNick: RT @biorxiv_bioinfo: VPAC: Variational projection for accurate clustering of single-cell transcriptomic data https://t.co/OBm5zsdk3A #bior…
TongZhou2017: RT @biorxiv_bioinfo: VPAC: Variational projection for accurate clustering of single-cell transcriptomic data https://t.co/OBm5zsdk3A #bior…
Github

VPAC

Repository: VPAC
User: ShengquanChen
Language: Python
Stargazers: 0
Subscribers: 1
Forks: 0
Open Issues: 0
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Authors: 4
Total Words: 7428
Unqiue Words: 2109

2.008 Mikeys
#5. Identification of flavone and its derivatives as potential inhibitors of transcriptional regulator LasR of Pseudomonas aeruginosa using virtual screening
Narek Nverovich Abelyan, Hovakim Grabski, Susanna Tiratsuyan
Antibiotic resistance is a global problem nowadays and in 2017 the World Health Organization published the list of bacteria for which treatment are urgently needed, where Pseudomonas aeruginosa is of critical priority. Current therapies lack efficacy because this organism creates biofilms conferring increased resistance to antibiotics and host immune responses. The strategy is to "not kill, but disarm" the pathogen and resistance will be developed slowly. It has been shown that LasI/LasR system is the main component of the quorum sensing system in P. aeruginosa. LasR is activated by the interaction with its native autoinducer. A lot flavones and their derivatives are used as antibacterial drug compounds. The purpose is to search compounds that will inhibit LasR. This leads to the inhibition of the synthesis of virulence factors thus the bacteria will be vulnerable and not virulent. We performed virtual screening using multiple docking programs for obtaining consensus predictions. The results of virtual screening suggest benzamides...
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biorxivpreprint: Identification of flavone and its derivatives as potential inhibitors of transcriptional regulator LasR of Pseudomonas aeruginosa using virtual screening https://t.co/RFWY9KZZsC #bioRxiv
biorxiv_bioinfo: Identification of flavone and its derivatives as potential inhibitors of transcriptional regulator LasR of Pseudomonas aeruginosa ... https://t.co/lCQJT94p0c #biorxiv_bioinfo
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Sample Sizes : None.
Authors: 3
Total Words: 4718
Unqiue Words: 1681

2.003 Mikeys
#6. Dynamic pseudo-time warping of complex single-cell trajectories
Van Hoan Do, Mislav Blažević, Pablo Monteagudo, Luka Borozan, Khaled Elbassioni, Sören Laue, Francisca Rojas Ringeling, Domagoj Matijević, Stefan Canzar
Single-cell RNA sequencing enables the construction of trajectories describing the dynamic changes in gene expression underlying biological processes such as cell differentiation and development. The comparison of single-cell trajectories under two distinct conditions can illuminate the differences and similarities between the two and can thus be a powerful tool. Recently developed methods for the comparison of trajectories rely on the concept of dynamic time warping (dtw), which was originally proposed for the comparison of two time series. Consequently, these methods are restricted to simple, linear trajectories. Here, we adopt and theoretically link arboreal matchings to dtw and propose an algorithm to compare complex trajectories that more realistically contain branching points that divert cells into different fates. We implement a suite of exact and heuristic algorithms suitable for the comparison of trajectories of different characteristics in our tool Trajan. Trajan automatically pairs similar biological processes between...
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biorxivpreprint: Dynamic pseudo-time warping of complex single-cell trajectories https://t.co/OkAxoBOnEL #bioRxiv
biorxiv_bioinfo: Dynamic pseudo-time warping of complex single-cell trajectories https://t.co/Fg4Bno5Kq0 #biorxiv_bioinfo
razoralign: Trajan: Dynamic pseudo-time warping of complex single-cell trajectories: https://t.co/qENX400080
StefanCanzar: Excited to present Trajan at RECOMB this year: Method to compare expression dynamics between #singlecell trajectories. #scRNAseq https://t.co/jHKoP8KspK https://t.co/r8XBspNMtf
gggtta: Dynamic pseudo-time warping of complex single-cell trajectories https://t.co/mgnuWsFxvS
Nandox_85: RT @biorxiv_bioinfo: Dynamic pseudo-time warping of complex single-cell trajectories https://t.co/Fg4Bno5Kq0 #biorxiv_bioinfo
Github

Single-cell trajectory alignment with Trajan

Repository: Trajan
User: canzarlab
Language: C++
Stargazers: 0
Subscribers: 3
Forks: 0
Open Issues: 0
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Sample Sizes : None.
Authors: 9
Total Words: 9595
Unqiue Words: 2575

2.0 Mikeys
#7. ESUCA: a pipeline for genome-wide identification of upstream open reading frames with evolutionarily conserved sequences and determination of the taxonomic range of their conservation
Hiro Takahashi, Noriya Hayashi, Yui Yamashita, Satoshi Naito, Anna Takahashi, Kazuyuki Fuse, Toshinori Endo, Shoko Kojima, Hitoshi Onouchi
Background: Some upstream open reading frames (uORFs) in the 5' leaders of eukaryotic mRNAs encode evolutionarily conserved functional peptides, such as cis-acting regulatory peptides that control main ORF (mORF) translation. To comprehensively identify uORFs with conserved peptide sequences (CPuORFs), we previously developed the BAIUCAS pipeline, in which uORF sequences are compared between a certain species and any others with available transcript sequence databases. However, further selection is needed to identify CPuORFs encoding functional peptides. The purpose of this study is to develop a novel pipeline to efficiently identify CPuORFs likely to encode functional peptides. Results: Here, we present the ESUCA pipeline. In addition to the function of BAIUCAS, ESUCA has the following new functions: 1) to identify CPuORFs likely to be conserved due to the functions of their encoded small peptides, not due to encoding parts of the mORF-encoded proteins; 2) to systematically calculate Ka/Ks ratios to assess whether uORF sequences...
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razoralign: ESUCA: a pipeline for genome-wide identification of upstream open reading frames with evolutionarily conserved sequences and determination of the taxonomic range of their conservation: https://t.co/GMNNK6bH1r
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Authors: 9
Total Words: 12631
Unqiue Words: 3269

1.999 Mikeys
#8. PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting
Annett Erkes, Stefanie Mücke, Maik Reschke, Jens Boch, Jan Grau
Plant-pathogenic Xanthomonas bacteria secret transcription activator-like effectors (TALEs) into host cells, where they act as transcriptional activators on plant target genes to support bacterial virulence. TALEs have a unique modular DNA-binding domain composed of tandem repeats. Two amino acids within each tandem repeat, termed repeat-variable diresidues, bind to contiguous nucleotides on the DNA sequence and determine target specificity. In this paper, we propose a novel approach for TALE target prediction to identify potential virulence targets. Our approach accounts for recent findings concerning TALE targeting, including frame-shift binding by repeats of aberrant lengths, and the flexible strand orientation of target boxes relative to the transcription start of the downstream target gene. The computational model can account for dependencies between adjacent RVD positions. Model parameters are learned from the wealth of quantitative data that have been generated over the last years. We benchmark the novel approach, termed...
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Repository: Jstacs
User: Jstacs
Language: Java
Stargazers: 0
Subscribers: 0
Forks: 0
Open Issues: 0
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Sample Sizes : None.
Authors: 5
Total Words: 15875
Unqiue Words: 4111

1.998 Mikeys
#9. BigMPI4py: Python module for parallelization of Big Data objects
Alex M. Ascensión, Marcos J. Araúzo-Bravo
Big Data analysis is a powerful discipline due to the growing number of areas where technologies extract huge amounts of knowledge from data, thus increasing the demand for storage and computational resources. Python was one of the 5 most used programming languages in 2018 and is widely used in Big Data. Parallelization in Python integrates High Performance Computing (HPC) communication protocols like Message Passing Interface (MPI) via mpi4py module. However, mpi4py does not support parallelization of objects greater than 2^31 bytes, common in Big Data projects. To overcome this limitation we developed BigMPI4py, a Python module that surpasses the parallelization capabilities of mpi4py, and supports object sizes beyond the 2^31 boundary and up to the RAM limit of the computer. BigMPI4py automatically determines, taking into account the data type, the optimal object division strategy for parallelization, and uses vectorized methods for arrays of numeric types, achieving higher parallelization efficiency. Our module has simpler...
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Tweets
biorxivpreprint: BigMPI4py: Python module for parallelization of Big Data objects https://t.co/YA5XpEsES7 #bioRxiv
biorxiv_bioinfo: BigMPI4py: Python module for parallelization of Big Data objects https://t.co/fUJmWCADrp #biorxiv_bioinfo
HumansAnalytics: RT @biorxivpreprint: BigMPI4py: Python module for parallelization of Big Data objects https://t.co/YA5XpEsES7 #bioRxiv
francois_sabot: RT @biorxiv_bioinfo: BigMPI4py: Python module for parallelization of Big Data objects https://t.co/fUJmWCADrp #biorxiv_bioinfo
cnachteg: RT @biorxiv_bioinfo: BigMPI4py: Python module for parallelization of Big Data objects https://t.co/fUJmWCADrp #biorxiv_bioinfo
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Sample Sizes : [5, 3]
Authors: 2
Total Words: 8786
Unqiue Words: 2502

1.998 Mikeys
#10. Feature Design for Protein Interface hotspots using KFC2 and Rosetta
Franziska Seeger, Anna Little, Yang Chen, Tina Woolf, Haiyan Cheng, Julie C Mitchell
Protein-protein interactions regulate many essential biological processes and play an important role in health and disease. The process of experimentally characterizing protein residues that contribute the most to protein-protein interaction affinity and specificity is laborious. Thus, developing models that accurately characterize hotspots at protein-protein interfaces provides important information about how to inhibit therapeutically relevant protein-protein interactions. During the course of the ICERM WiSDM workshop 2017, we combined the KFC2a protein-protein interaction hotspot prediction features with Rosetta scoring function terms and interface filter metrics. A 2-way and 3-way forward selection strategy was employed to train support vector machine classifiers, as was a reverse feature elimination strategy. From these results, we identified subsets of KFC2a and Rosetta combined features that show improved performance over KFC2a features alone.
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FranziskaSeeger: Our work on "Feature Design for Protein Interface hotspots using KFC2 and Rosetta" using #SVMs in now on @biorxivpreprint #preprint #proteins #proteindesign #machinelearning #PPIs https://t.co/N5HkrGdP2D
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Authors: 6
Total Words: 9206
Unqiue Words: 2645

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