Response envelope analysis for quantitative evaluation of drug combinations

Abstract Motivation The concept of synergy between two agents, over a century old, is important to the fields of biology, chemistry, pharmacology and medicine. A key step in drug combination analysis is the selection of an additivity model to identify combination effects including synergy, additivity and antagonism. Existing methods for identifying and interpreting those combination effects have limitations. Results We present here a computational framework, termed response envelope analysis (REA), that makes use of 3D response surfaces formed by generalized Loewe Additivity and Bliss Independence models of interaction to evaluate drug combination effects. Because the two models imply two extreme limits of drug interaction (mutually exclusive and mutually non-exclusive), a response envelope defined by them provides a quantitatively stringent additivity model for identifying combination effects without knowing the inhibition mechanism. As a demonstration, we apply REA to representative published data from large screens of anticancer and antibiotic combinations. We show that REA is more accurate than existing methods and provides more consistent results in the context of cross-experiment evaluation. Availability and implementation The open-source software package associated with REA is available at: https://github.com/4dsoftware/rea. Supplementary information Supplementary data are available at Bioinformatics online.

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Ribo-ODDR: Oligo design pipeline for experiment-specific rRNA depletion in ribo-seq

Bioinformatics ◽

10.1093/bioinformatics/btab171 ◽

2021 ◽

Author(s):

Ferhat Alkan ◽

Joana Silva ◽

Eric Pintó Barberà ◽

William J Faller

Keyword(s):

Ribosome Profiling ◽

Supplementary Information ◽

Experimental Conditions ◽

Computational Framework ◽

Rna Translation ◽

Rrna Depletion ◽

Selection For ◽

Nucleotide Resolution ◽

User Friendly ◽

Oligo Design

Abstract Motivation Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Results Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a “one-size-fits-all” approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Availability Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR Supplementary information Supplementary data are available at Bioinformatics online.

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TAIJI: approaching experimental replicates-level accuracy for drug synergy prediction

Bioinformatics ◽

10.1093/bioinformatics/bty955 ◽

2018 ◽

Vol 35 (13) ◽

pp. 2338-2339 ◽

Cited By ~ 5

Author(s):

Hongyang Li ◽

Shuai Hu ◽

Nouri Neamati ◽

Yuanfang Guan

Keyword(s):

Drug Combination ◽

High Performance ◽

Synergistic Effects ◽

Field Performance ◽

Supplementary Information ◽

Drug Synergism ◽

High Throughput Drug Screening ◽

Current State ◽

Candidate Drug ◽

High Prediction

Abstract Motivation Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs. Results Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance. Availability and implementation TAIJI is freely available on GitHub (https://github.com/GuanLab/TAIJI). It is functional with built-in Perl and Python. Supplementary information Supplementary data are available at Bioinformatics online.

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Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data

Bioinformatics ◽

10.1093/bioinformatics/btz769 ◽

2019 ◽

Cited By ~ 3

Author(s):

Runpu Chen ◽

Le Yang ◽

Steve Goodison ◽

Yijun Sun

Keyword(s):

Deep Learning ◽

Cluster Structure ◽

Data Representation ◽

Supplementary Information ◽

High Dimensional ◽

Breast Cancer Dataset ◽

Cancer Dataset ◽

Cancer Subtypes ◽

Novel Approach ◽

Open Source Software Package

Abstract Motivation Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes. Results To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. Availability and implementation An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html. Supplementary information Supplementary data are available at Bioinformatics online.

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Prediction of drug combination effects with a minimal set of experiments

Nature Machine Intelligence ◽

10.1038/s42256-019-0122-4 ◽

2019 ◽

Vol 1 (12) ◽

pp. 568-577 ◽

Cited By ~ 9

Author(s):

Aleksandr Ianevski ◽

Anil K. Giri ◽

Prson Gautam ◽

Alexander Kononov ◽

Swapnil Potdar ◽

...

Keyword(s):

Drug Combination ◽

Minimal Set ◽

Combination Effects

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A parallel computational framework for ultra-large-scale sequence clustering analysis

Bioinformatics ◽

10.1093/bioinformatics/bty617 ◽

2018 ◽

Vol 35 (3) ◽

pp. 380-388 ◽

Cited By ~ 2

Author(s):

Wei Zheng ◽

Qi Mao ◽

Robert J Genco ◽

Jean Wactawski-Wende ◽

Michael Buck ◽

...

Keyword(s):

Parallel Computing ◽

High Performance ◽

Large Scale ◽

De Novo ◽

Rapid Development ◽

Operational Taxonomic Unit ◽

Supplementary Information ◽

Computational Framework ◽

Speed Up ◽

Scale Sequence

Abstract Motivation The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing. Results In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efficiency. The proposed framework was implemented on Apache Spark, which allows for easy and efficient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can significantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (∼2.2B reads, 437 GB) demonstrated the excellent scalability of the proposed method. Availability and implementation Open-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/~yijunsun/lab/SLAD.html. Supplementary information Supplementary data are available at Bioinformatics online.

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Abstract B51: Image-based classification of cancer drug combination response surfaces

10.1158/1557-3265.pms14-b51 ◽

2015 ◽

Author(s):

Manway Liu ◽

Thomas Horn ◽

Matthew Greene ◽

Joseph Lehar

Keyword(s):

Drug Combination ◽

Response Surfaces ◽

Cancer Drug

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Ensemble learning for classifying single-cell data and projection across reference atlases

Bioinformatics ◽

10.1093/bioinformatics/btaa137 ◽

2020 ◽

Vol 36 (11) ◽

pp. 3585-3587

Author(s):

Lin Wang ◽

Francisca Catalan ◽

Karin Shamardani ◽

Husam Babikir ◽

Aaron Diaz

Keyword(s):

Single Cell ◽

Cell Types ◽

Status Quo ◽

Supplementary Information ◽

Published Data ◽

Supplementary Data ◽

Cell Type ◽

Low Sensitivity ◽

Project Data ◽

Cell Data

Abstract Summary Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms. Availability and implementation https://github.com/diazlab/ELSA Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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