Circulating tumor DNA (ctDNA) refers to small fragments of tumor DNA in the total cell free DNA (cfDNA) from cancer patients. Detecting ctDNA in plasma has provided a promising, non-invasive liquid biopsy approach that could be utilized for cancer diagnosis and treatments. Depending on many factors, the quantity of ctDNA can be very low in the total cfDNA especially in early stage cancers, which posted a great challenge for ultra-sensitive detection with high specificity. Previously, we published TNER, an effective noise reducing algorithm based on tri-nucleotide context (TNC) data from healthy subject samples to polish background noise before calling mutations. It offers great specificity in mutation detection however it might over-polish true mutations when ctDNA concentration is very low. To improve its detection sensitivity in samples with low ctDNA concentration, we developed TNER2, a significant extension of TNER with a refined background polishing process by leveraging somatic mutation patterns from known cancer mutation data. Since there is a distinct difference between TNC pattern in cancer and healthy sample, TNER2 enables a more sensitive analysis on ctDNA data for early detection of cancer.

BACKGROUND: Ultra-deep next-generation sequencing of circulating tumor DNA (ctDNA) holds great promise as a tool for the early detection of cancer and for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing complicates mutation detection. RESULTS: To achieve high accuracy of variant calling via better distinguishing low-frequency ctDNA mutations from background errors, we introduce TNER (Tri-Nucleotide Error Reducer), a novel background error suppression method that provides a robust estimation of background noise to reduce sequencing errors. The results on both simulated data and real data from healthy subjects demonstrate that the proposed algorithm consistently outperforms a current, state-of-the-art, position-specific error polishing model, particularly when the sample size of healthy subjects is small. CONCLUSIONS: TNER significantly enhances the specificity of downstream ctDNA mutation detection without sacrificing sensitivity. The tool is publicly available at https://github.com/ctDNA/TNER .

SUMMARY: Transcriptional profiling still remains one of the most popular techniques for identifying relevant biomarkers in patient samples. However, heterogeneity in the population leads to poor statistical evidence for selection of most relevant biomarkers to pursue. In particular, human transcriptional differences can be subtle, making it difficult to tease out real differentially expressed biomarkers from the variability inherent in the population. To address this issue, we propose a simple statistical technique that identifies differentially expressed probes in heterogeneous populations as compared with controls. AVAILABILITY AND IMPLEMENTATION: The algorithm has been implemented in Java and available at www.sourceforge.net/projects/probeselect.

Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86--a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28.

The long-standing problem of constructing protein structure alignments is of central importance in computational biology. The main goal is to provide an alignment of residue correspondences, in order to identify homologous residues across chains. A critical next step of this is the alignment of protein complexes and their interfaces. Here, we introduce the program CMAPi, a two-dimensional dynamic programming algorithm that, given a pair of protein complexes, optimally aligns the contact maps of their interfaces: it produces polynomial-time near-optimal alignments in the case of multiple complexes. We demonstrate the efficacy of our algorithm on complexes from PPI families listed in the SCOPPI database and from highly divergent cytokine families. In comparison to existing techniques, CMAPi generates more accurate alignments of interacting residues within families of interacting proteins, especially for sequences with low similarity. While previous methods that use an all-atom based representation of the interface have been successful, CMAPi's use of a contact map representation allows it to be more tolerant to conformational changes and thus to align more of the interaction surface. These improved interface alignments should enhance homology modeling and threading methods for predicting PPIs by providing a basis for generating template profiles for sequence-structure alignment.

The method currently most used for probeset detection calls on Affymetrix GeneChipreg Human Genome Arrays is provided as part of the MAS5 software. The MAS method uses Wilcoxon statistics for determining presence-absence (MAS-P/A) calls. However, MAS-P/A is only usable with MAS5 processing, which requires the use of both perfect match (PM) and mismatch (MM) probe data in order to call the resulting probeset present or absent. A considerable amount of recent research has convincingly shown that using MM data in gene expression analysis may be problematic. The RMA method, which uses PM data only, is one method that has been developed in response to this. However, there is no publicly available method that works with PM-only expression data to establish presence or absence of genes from the probesets in microarray data. It seems desirable to decouple the method used to generate gene expression values from the method used to make gene detection calls. We have therefore developed a statistical method in R, called presence-absence calls with negative probesets (PANP) which uses sets of Affymetrix-reported probes with no known hybridization partners on two chip sets: HG-U133A and HG-U133 Plus 2.0. PANP allows the use of any Affymetrix microarray data preprocessing method to generate expression values, including PM-only methods as well as PM and MM methods. We present our results on PANP and its performance using the set of 28 HG-U133A chips from a published Affymetrix Latin squares spike-in dataset as well as an internal TaqMan-validated human tissue dataset on the HG-U133 Plus 2.0 chipsets. We And that using these datasets, PANP out-performs the MAS-PA method in several metrics of accuracy and precision using a variety of preprocessing methods: RMA, GCRMA, and even MAS5 itself. PANP out-performs MAS-P/A in probeset detection across a full range of concentrations, especially with low concentration transcripts. An R software package has been prepared for PANP and is available in R as part of the Bioconductor package release at http://www.bioconductor.org.

The wealth of protein sequence and structure data is greater than ever, thanks to the ongoing Genomics and Structural Genomics projects. The information available through such efforts needs to be analysed by new methods that combine both databases. One important result of genomic sequence analysis is the inference of functional homology among proteins. Until recently sequence similarity comparison was the only method for homologue inference. The new fold recognition approach reviewed in this paper enhances sequence comparison methods by including structural information in the process of protein comparison. This additional information often allows for the detection of similarities that cannot be found by methods that only use sequence information.

We present a protein fold recognition method that uses a comprehensive statistical interpretation of structural Hidden Markov Models (HMMs). The structure/fold recognition is done by summing the probabilities of all sequence-to-structure alignments Conventionally, Boltzmann statistics dictate that the optimal alignment can give an estimate of the lowest free energy of the sequence conformation imposed by the structural model. The alignment is optimized for a scoring function that is interpreted as a free energy of an amino acid in a structural environment. Near-optimal alignments are ignored, regardless of how likely they might be compared to the optimal alignment. Here we investigate an alternative view. A structure model can be seen as a statistical representation of an ensemble of similar structures. The optimal alignment is always the most probable, but sub-optimal alignments may have comparable probabilities. These sub-optimal alignments can be interpreted as optimal alignments to the “other” structures from the ensemble or optimal alignments under minor fluctuations in the scoring function. Summing probabilities for all alignments gives an estimate of sequence-model compatibility. We have built a set of structural HMMs for 188 protein structures, and have compared two methods for identifying the structure compatible with a sequence: by the optimal alignment probability and by the total probability. Fold recognition by total probability was 40% more accurate than fold recognition by the optimal alignment probability.

Article Free Access Share on A method for optimal design of a threading scoring function Authors: Jadwiga R. Bieńkowska BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Robert G. Rogers BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Temple F. Smith BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile Authors Info & Claims RECOMB '99: Proceedings of the third annual international conference on Computational molecular biologyApril 1999 Pages 25–32https://doi.org/10.1145/299432.299446Published:01 April 1999Publication History 1citation7,945DownloadsMetricsTotal Citations1Total Downloads7,945Last 12 Months4Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Article The threading approach to the inverse protein folding problem Share on Authors: Temple F. Smith BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Loredana Lo Conte BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA and Department of Computer Science, College of Arts and Sciences, Boston University, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA and Department of Computer Science, College of Arts and Sciences, Boston University, Boston, MAView Profile , Jadwiga Bienkowska BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Bob Rogers BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Chrysanthe Gaitatzes BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Richard Lathrop Department of Infomation and Computer Science, University of California, Irvine, CA Department of Infomation and Computer Science, University of California, Irvine, CAView Profile Authors Info & Claims RECOMB '97: Proceedings of the first annual international conference on Computational molecular biologyJanuary 1997 Pages 287–292https://doi.org/10.1145/267521.267887Online:19 January 1997Publication History 3citation95DownloadsMetricsTotal Citations3Total Downloads95Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access