Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals.

Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package.

Availability: http://maq.sourceforge.net 

Contact: [email protected]

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Fast and accurate short read alignment with Burrows–Wheeler transform

Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

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Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

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An integrated encyclopedia of DNA elements in the human genome

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.

/pdf/model-based-analysis-of-chip-seq-macs-11m6g885d3.pdf

Model-based Analysis of ChIP-Seq (MACS)

This article discusses the effect of segregation of histopathology images data into three sets; training set for training machine learning model, validation set for model selection and test set for testing model performance. We found that one must be cautious when segregating histological images data (slides) into training, validation and test sets because subtle mishandling of data can introduce data leakage and gives illusively good results on the test set. We performed this study on gene mutation prediction performance by using the deep neural network in the paper of Coudray et al. [1]. By using the provided code and the same set of data, we discovered that data segregation method of the paper suffered from a data leakage problem [2]. The paper pools all the slides from all patients and then segregates them exclusively into training, validation and test sets. In this way, none of the slides is used in more than one set. This seems to be a clean separation of the data. However, the paper did not consider that some slides were strongly correlated. For example, if the tumor of a patient is cut and stained to produce multiple slides, these slides are strongly correlated. If one slide is used for training and another one is used for testing, essentially, the deep neural network can memorize the pattern on the slide in the training set and apply this memory on the slide in the test set. Hence, by memorization, the deep neural network can predict very well on the slide in the test set. This mechanism of prediction is not useful in a practical clinical setting since no two tumors are the same in the real world. In this real setting, we demand the deep neural network to generalize across patients and tumors. Hereafter, we call this way of data segregation slide-level segregation. There is a better way to perform data segregation that is compatible for deployment of deep learning model in practical clinical settings. First, the patients are segregated exclusively into training, validation and test sets. All the slides belonging to the patients in the training set are used solely for training. Similarly, all the slides belonging to the patients in the test set are used for testing only. Segregation of data in this way forces the deep neural network to generalize across patients. We call this way of data segregation patient-level segregation. In slide-level segregation approach analysis, we obtained similar results to that presented in the paper by Coudray et al. [1]: overall performance on the test set was good. However, it was illusory due to data leakage. The model gave very good testing results on the slides that come from a patient who also has slides in the training set. On the other hand, the test result was quite bad on the slides that come from a patient who does not have any slides in the training set. Hereafter, we call the slide in the test set as seen-patient data if the corresponding patient also has some slides in the training set. Otherwise, the slide in the test set is called unseen-patient data if the corresponding patient does not have slides in the training set. Furthermore, we analyzed performance of the model on the data segregated by the patient-level segregation approach. Note that, in this approach, all patients in the test set mimics the real world clinical workflow. We observed a significant drop in the performance of the model on the test set of patient-level segregation approach compared to the performance on the test set of slide-level segregation approach. Moreover, the performance of the model on the test set of patient-level segregation approach was very similar to the performance on the unseen-patients data in the test set of slide-level segregation approach. Hence, we conclude that patient-level segregation approach is crucial and appropriate to simulate real world scenario, where each patient in the test set can be thought as a patient walking into clinic tomorrow.

/pdf/training-machine-learning-models-on-patient-level-data-2htfxokqes.pdf

Training machine learning models on patient level data segregation is crucial in practical clinical applications

The detection of palindrome sequences in DNA sequences is important in biology Palindrome is also called inverted repeat and is an important element in the human genome This paper presents a novel technique of applying modified periodic transform and signal processing methods in the domain of palindrome detection Palindrome has recently received a lot attention in the research community due to its significance in identifying several key areas in a genome in relation to gene amplification, gene regulation and diseases

/pdf/detection-of-palindromes-in-dna-sequences-using-periodicity-gi6zvz86r4.pdf

Detection of palindromes in DNA sequences using periodicity transform

Many important biological processes, such as the signaling pathways, require protein-protein interactions (PPIs) that are designed for fast response to stimuli. These interactions are usually transient, easily formed, and disrupted, yet specific. Many of these transient interactions involve the binding of a protein domain to a short stretch (3-10) of amino acid residues, which can be characterized by a sequence pattern, i.e., a short linear motif (SLiM). We call these interacting domains and motifs domain-SLiM interactions. Existing methods have focused on discovering SLiMs in the interacting proteins' sequence data. With the recent increase in protein structures, we have a new opportunity to detect SLiMs directly from the proteins' 3D structures instead of their linear sequences. In this chapter, we describe a computational method called SLiMDIet to directly detect SLiMs on domain interfaces extracted from 3D structures of PPIs. SLiMDIet comprises two steps: (1) interaction interfaces belonging to the same domain are extracted and grouped together using structural clustering and (2) the extracted interaction interfaces in each cluster are structurally aligned to extract the corresponding SLiM. Using SLiMDIet, de novo SLiMs interacting with protein domains can be computationally detected from structurally clustered domain-SLiM interactions for PFAM domains which have available 3D structures in the PDB database.

Discovering interacting domains and motifs in protein-protein interactions.

There are different multiple instance learning (MIL) pooling filters used in MIL models. In this paper, we study the effect of different MIL pooling filters on the performance of MIL models in real world MIL tasks. We designed a neural network based MIL framework with 5 different MIL pooling filters: `max', `mean', `attention', `distribution' and `distribution with attention'. We also formulated 5 different MIL tasks on a real world lymph node metastases dataset. We found that the performance of our framework in a task is different for different filters. We also observed that the performances of the five pooling filters are also different from task to task. Hence, the selection of a correct MIL pooling filter for each MIL task is crucial for better performance. Furthermore, we noticed that models with `distribution' and `distribution with attention' pooling filters consistently perform well in almost all of the tasks. We attribute this phenomena to the amount of information captured by `distribution' based pooling filters. While point estimate based pooling filters, like `max' and `mean', produce point estimates of distributions, `distribution' based pooling filters capture the full information in distributions. Lastly, we compared the performance of our neural network model with `distribution' pooling filter with the performance of the best MIL methods in the literature on classical MIL datasets and our model outperformed the others.

Studying The Effect of MIL Pooling Filters on MIL Tasks.

Exact determination of a gene network is required to discover the higher-order structures of an organism and to interpret its behavior. Most research work in learning gene networks either assumes that there is no time delay in gene expression or that there is a constant time delay. This paper shows how Bayesian Networks can be applied to represent multi-time delay relationships as well as directed loops. The intractability of the network learning algorithm is handled by using an improved mutual information criterion. Also, a new structure learning algorithm, "Learning By Modification", is proposed to learn the sparse structure of a gene network. The experimental results on synthetic data and real data show that our method is more accurate in determining the gene structure as compared to the traditional methods. Even transcriptional loops spanning over the whole cell can be detected by our algorithm.

Wing-Kin Sung

Papers

Training machine learning models on patient level data segregation is crucial in practical clinical applications

Detection of palindromes in DNA sequences using periodicity transform

Discovering interacting domains and motifs in protein-protein interactions.

Studying The Effect of MIL Pooling Filters on MIL Tasks.

Learning gene network using time-delayed bayesian network