MOTIVATION: The use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next-generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs. RESULTS: We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples. AVAILABILITY AND IMPLEMENTATION: ABEMUS is cross-platform and can be installed as R package. The source code is maintained on Github at http://github.com/cibiobcg/abemus, and it is also available at CRAN official R repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effective biomedical literature retrieval (BLR) plays a central role inprecision medicine informatics. In this paper, we propose GRAPHENE,which is a deep learning based framework for precise BLR. GRAPHENEconsists of three main different modules 1) graph-augmented doc-ument representation learning; 2) query expansion and represen-tation learning and 3) learning to rank biomedical articles. Thegraph-augmented document representation learning module con-structs a document-concept graph containing biomedical conceptnodes and document nodes so that global biomedical related con-cept from external knowledge source can be captured, which isfurther connected to a BiLSTM so both local and global topics canbe explored. Query expansion and representation learning moduleexpands the query with abbreviations and different names, and thenbuilds a CNN-based model to convolve the expanded query andobtain a vector representation for each query. Learning to rank min-imizes a ranking loss between biomedical articles with the queryto learn the retrieval function. Experimental results on applyingour system to TREC Precision Medicine track data are provided todemonstrate its effectiveness.

OBJECTIVE: This paper describes the Precision Medicine Knowledge Base (PMKB; https://pmkb.weill.cornell.edu ), an interactive online application for collaborative editing, maintenance, and sharing of structured clinical-grade cancer mutation interpretations. MATERIALS AND METHODS: PMKB was built using the Ruby on Rails Web application framework. Leveraging existing standards such as the Human Genome Variation Society variant description format, we implemented a data model that links variants to tumor-specific and tissue-specific interpretations. Key features of PMKB include support for all major variant types, standardized authentication, distinct user roles including high-level approvers, and detailed activity history. A REpresentational State Transfer (REST) application-programming interface (API) was implemented to query the PMKB programmatically. RESULTS: At the time of writing, PMKB contains 457 variant descriptions with 281 clinical-grade interpretations. The EGFR, BRAF, KRAS, and KIT genes are associated with the largest numbers of interpretable variants. PMKB's interpretations have been used in over 1500 AmpliSeq tests and 750 whole-exome sequencing tests. The interpretations are accessed either directly via the Web interface or programmatically via the existing API. DISCUSSION: An accurate and up-to-date knowledge base of genomic alterations of clinical significance is critical to the success of precision medicine programs. The open-access, programmatically accessible PMKB represents an important attempt at creating such a resource in the field of oncology. CONCLUSION: The PMKB was designed to help collect and maintain clinical-grade mutation interpretations and facilitate reporting for clinical cancer genomic testing. The PMKB was also designed to enable the creation of clinical cancer genomics automated reporting pipelines via an API.

In this review, we describe key components of a computational infrastructure for a precision medicine program that is based on clinical-grade genomic sequencing. Specific aspects covered in this review include software components and hardware infrastructure, reporting, integration into Electronic Health Records for routine clinical use and regulatory aspects. We emphasize informatics components related to reproducibility and reliability in genomic testing, regulatory compliance, traceability and documentation of processes, integration into clinical workflows, privacy requirements, prioritization and interpretation of results to report based on clinical needs, rapidly evolving knowledge base of genomic alterations and clinical treatments and return of results in a timely and predictable fashion. We also seek to differentiate between the use of precision medicine in germline and cancer.

UNLABELLED: The functional annotation of variants obtained through sequencing projects is generally assumed to be a simple intersection of genomic coordinates with genomic features. However, complexities arise for several reasons, including the differential effects of a variant on alternatively spliced transcripts, as well as the difficulty in assessing the impact of small insertions/deletions and large structural variants. Taking these factors into consideration, we developed the Variant Annotation Tool (VAT) to functionally annotate variants from multiple personal genomes at the transcript level as well as obtain summary statistics across genes and individuals. VAT also allows visualization of the effects of different variants, integrates allele frequencies and genotype data from the underlying individuals and facilitates comparative analysis between different groups of individuals. VAT can either be run through a command-line interface or as a web application. Finally, in order to enable on-demand access and to minimize unnecessary transfers of large data files, VAT can be run as a virtual machine in a cloud-computing environment. AVAILABILITY AND IMPLEMENTATION: VAT is implemented in C and PHP. The VAT web service, Amazon Machine Image, source code and detailed documentation are available at vat.gersteinlab.org.

SUMMARY: The advent of next-generation sequencing for functional genomics has given rise to quantities of sequence information that are often so large that they are difficult to handle. Moreover, sequence reads from a specific individual can contain sufficient information to potentially identify and genetically characterize that person, raising privacy concerns. In order to address these issues, we have developed the Mapped Read Format (MRF), a compact data summary format for both short and long read alignments that enables the anonymization of confidential sequence information, while allowing one to still carry out many functional genomics studies. We have developed a suite of tools (RSEQtools) that use this format for the analysis of RNA-Seq experiments. These tools consist of a set of modules that perform common tasks such as calculating gene expression values, generating signal tracks of mapped reads and segmenting that signal into actively transcribed regions. Moreover, the tools can readily be used to build customizable RNA-Seq workflows. In addition to the anonymization afforded by MRF, this format also facilitates the decoupling of the alignment of reads from downstream analyses. AVAILABILITY AND IMPLEMENTATION: RSEQtools is implemented in C and the source code is available at http://rseqtools.gersteinlab.org/.

Open source and open data have been driving forces in bioinformatics in the past. However, privacy concerns may soon change the landscape, limiting future access to important data sets, including personal genomics data. Here we survey this situation in some detail, describing, in particular, how the large scale of the data from personal genomic sequencing makes it especially hard to share data, exacerbating the privacy problem. We also go over various aspects of genomic privacy: first, there is basic identifiability of subjects having their genome sequenced. However, even for individuals who have consented to be identified, there is the prospect of very detailed future characterization of their genotype, which, unanticipated at the time of their consent, may be more personal and invasive than the release of their medical records. We go over various computational strategies for dealing with the issue of genomic privacy. One can "slice" and reformat datasets to allow them to be partially shared while securing the most private variants. This is particularly applicable to functional genomics information, which can be largely processed without variant information. For handling the most private data there are a number of legal and technological approaches-for example, modifying the informed consent procedure to acknowledge that privacy cannot be guaranteed, and/or employing a secure cloud computing environment. Cloud computing in particular may allow access to the data in a more controlled fashion than the current practice of downloading and computing on large datasets. Furthermore, it may be particularly advantageous for small labs, given that the burden of many privacy issues falls disproportionately on them in comparison to large corporations and genome centers. Finally, we discuss how education of future genetics researchers will be important, with curriculums emphasizing privacy and data security. However, teaching personal genomics with identifiable subjects in the university setting will, in turn, create additional privacy issues and social conundrums.

In the last decade, high-throughput technologies such as DNA and tissue microarrays (TMAs) have become a means of large-scale investigation of gene expression, providing a plethora of new biomedical data in a relatively short time. Data collection and organization are critical aspects in this process to ensure the quality and reliability of future data interpretation. In this work, we propose a comprehensive approach to handle TMA data with the aim of supporting and promoting biomarker development. We describe a web-based system for the complete management of tissue microarray data in the field of pathology. The system has been in use since June, 2003. Our approach includes automatic localization and identification of tissue microarray samples, and quantitative image analysis that allows high-throughput screening of TMAs by ensuring nonsubjective measures and novel prognosis associations. In this paper, we present the architecture and the components of this system.

We describe the multi-modal browsing system, developed by us, that allows to add automatic speech recognition and text to speech functions to standard Internet browsers. The system is based on the temporal synchronization of HTML and VoiceXML documents. It was developed starting from a real Web application designed for a medical domain (i.e. an electronic patient record adopted in the oncology unit of an Italian hospital). We have introduced the possibility to define the multi-modal interaction by means of a single XML document. System evaluation is being carried out on data collected during the usage of the system in the hospital.

Hepatocellular carcinoma (HCC) is one of the commonest fatal tumors, and it is usually diagnosed at a late stage, when effective treatment is very difficult. Unfortunately early diagnosis of HCC is almost mandatory in terms of patient survival, but is represents a very difficult task. Detailed histological characteristics of small HCC and precursor lesions in histological diagnosis are needed to refine the contribution of histopathology to the management of patients with HCC. In this context a possible approach regards the implementation of knowledge extraction process based on a labeled data set of cases. Our data set is made of 212 cases, including early malignant nodules, benign ones, and some uncertain cases. A set of 11 histopathological and morphological features described each case. We applied statistical and machine learning algorithms both for feature selection and classification of nodules. In particular, we implemented an ensemble of classifiers in a straightforward way. Moreover, this ensemble provides physicians with a rough level of confidence of the proposed diagnoses, taking into account the importance of the interpretability of automated output. Feature selection approach was validated; actually a subset of four histological features performed at least as the whole set of features in the classification of the malignant and benign nodules. Ensemble of learning machines can be a tool to extract new knowledge from a set of data and improve comprehensibility of computerized output.