Metric Learning in Histopathological Image Classification: Opening the Black Box.

The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process. The classification of these images is a relevant aid for physicians who have to process a large number of images in long and repetitive tasks. This work proposes the adoption of metric learning that, beyond the task of classifying images, can provide additional information able to support the decision of the classification system. In particular, triplet networks have been employed to create a representation in the embedding space that gathers together images of the same class while tending to separate images with different labels. The obtained representation shows an evident separation of the classes with the possibility of evaluating the similarity and the dissimilarity among input images according to distance criteria. The model has been tested on the BreakHis dataset, a reference and largely used dataset that collects breast cancer images with eight pathology labels and four magnification levels. Our proposed classification model achieves relevant performance on the patient level, with the advantage of providing interpretable information for the obtained results, which represent a specific feature missed by the all the recent methodologies proposed for the same purpose.

Structural and Dynamic Disturbances Revealed by Molecular Dynamics Simulations Predict the Impact on Function of CCT5 Chaperonin Mutations Associated with Rare Severe Distal Neuropathies.

Mutations in genes encoding molecular chaperones, for instance the genes encoding the subunits of the chaperonin CCT (chaperonin containing TCP-1, also known as TRiC), are associated with rare neurodegenerative disorders. Using a classical molecular dynamics approach, we investigated the occurrence of conformational changes and differences in physicochemical properties of the CCT5 mutations His147Arg and Leu224Val associated with a sensory and a motor distal neuropathy, respectively. The apical domain of both variants was substantially but differently affected by the mutations, although these were in other domains. The distribution of hydrogen bonds and electrostatic potentials on the surface of the mutant subunits differed from the wild-type molecule. Structural and dynamic analyses, together with our previous experimental data, suggest that genetic mutations may cause different changes in the protein-binding capacity of CCT5 variants, presumably within both hetero- and/or homo-oligomeric complexes. Further investigations are necessary to elucidate the molecular pathogenic pathways of the two variants that produce the two distinct phenotypes. The data and clinical observations by us and others indicate that CCT chaperonopathies are more frequent than currently believed and should be investigated in patients with neuropathies.

Induction of 2-hydroxycatecholestrogens O-methylation: A missing puzzle piece in diagnostics and treatment of lung cancer.

Lung cancer is one of the most common cancers worldwide, causing nearly one million deaths each year. Herein, we present the effect of 2-methoxyestradiol (2-ME), the endogenous metabolite of 17ß-estradiol (E2), on non-small cell lung cancer (NSCLC) cells. We observed that 2-ME reduced the viability of lung adenocarcinoma in two-dimensional (2D) and three-dimensional (3D) spheroidal A549 cell culture models. Molecular modeling was carried out aiming to visualize amino acid residues within binding pockets of the acyl-protein thioesterases, namely 1 (APT1) and 2 (APT2), and thus to identify which ones were more likely involved in the interaction with 2-ME. Our findings suggest that 2-ME acts as an APT1 inhibitor enhancing protein palmitoylation and oxidative stress phenomena in the lung cancer cell. In order to support our data, metabolomics of blood serum from NSCLC patients was also performed. Moreover, computational analysis suggests that 2-ME as compared to other estrogen metabolism intermediates is relatively safe in terms of its possible non-receptor bioactivity within healthy human cells due to a very low electrophilic potential and hence no substantial risk of spontaneous covalent modification of biologically protective nucleophiles. We propose that 2-ME can be used as a selective tumor biomarker in the course of certain types of lung cancers and possibly as a therapeutic adjuvant or neoadjuvant.

Muscle Histopathological Abnormalities in a Patient With a CCT5 Mutation Predicted to Affect the Apical Domain of the Chaperonin Subunit.

Recognition of diseases associated with mutations of the chaperone system genes, e.g., chaperonopathies, is on the rise. Hereditary and clinical aspects are established, but the impact of the mutation on the chaperone molecule and the mechanisms underpinning the tissue abnormalities are not. Here, histological features of skeletal muscle from a patient with a severe, early onset, distal motor neuropathy, carrying a mutation on the CCT5 subunit (MUT) were examined in comparison with normal muscle (CTR). The MUT muscle was considerably modified; atrophy of fibers and disruption of the tissue architecture were prominent, with many fibers in apoptosis. CCT5 was diversely present in the sarcolemma, cytoplasm, and nuclei in MUT and in CTR and was also in the extracellular space; it colocalized with CCT1. In MUT, the signal of myosin appeared slightly increased, and actin slightly decreased as compared with CTR. Desmin was considerably delocalized in MUT, appearing with abnormal patterns and in precipitates. Alpha-B-crystallin and Hsp90 occurred at lower signals in MUT than in CTR muscle, appearing also in precipitates with desmin. The abnormal features in MUT may be the consequence of inactivity, malnutrition, denervation, and failure of protein homeostasis. The latter could be at least in part caused by malfunction of the CCT complex with the mutant CCT5 subunit. This is suggested by the results of the in silico analyses of the mutant CCT5 molecule, which revealed various abnormalities when compared with the wild-type counterpart, mostly affecting the apical domain and potentially impairing chaperoning functions. Thus, analysis of mutated CCT5 in vitro and in vivo is anticipated to provide additional insights on subunit involvement in neuromuscular disorders.

Green Tea Catechins Induce Inhibition of PTP1B Phosphatase in Breast Cancer Cells with Potent Anti-Cancer Properties: In Vitro Assay, Molecular Docking, and Dynamics Studies.

The catechins derived from green tea possess antioxidant activity and may have a potentially anticancer effect. PTP1B is tyrosine phosphatase that is oxidative stress regulated and is involved with prooncogenic pathways leading to the formation of a.o. breast cancer. Here, we present the effect of selected green tea catechins on enzymatic activity of PTP1B phosphatase and viability of MCF-7 breast cancer cells. We showed also the computational analysis of the most effective catechin binding with a PTP1B molecule. We observed that epigallocatechin, epigallocatechin gallate, epicatechin, and epicatechin gallate may decrease enzymatic activity of PTP1B phosphatase and viability of MCF-7 cells. Conclusions: From the tested compounds, epigallocatechin and epigallocatechin gallate were the most effective inhibitors of the MCF-7 cell viability. Moreover, epigallocatechin was also the strongest inhibitor of PTP1B activity. Computational analysis allows us also to conclude that epigallocatechin is able to interact and bind to PTP1B. Our results suggest also the most predicted binding site to epigallocatechin binding to PTP1B.

BITS2019: the sixteenth annual meeting of the Italian society of bioinformatics.

The 16th Annual Meeting of the Bioinformatics Italian Society was held in Palermo, Italy, on June 26-28, 2019. More than 80 scientific contributions were presented, including 4 keynote lectures, 31 oral communications and 49 posters. Also, three workshops were organised before and during the meeting. Full papers from some of the works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

Human molecular chaperones share with SARS-CoV-2 antigenic epitopes potentially capable of eliciting autoimmunity against endothelial cells: possible role of molecular mimicry in COVID-19.

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2), the cause of COVID-19 disease, has the potential to elicit autoimmunity because mimicry of human molecular chaperones by viral proteins. We compared viral proteins with human molecular chaperones, many of which are heat shock proteins, to determine if they share amino acid-sequence segments with immunogenic-antigenic potential, which can elicit cross-reactive antibodies and effector immune cells with the capacity to damage-destroy human cells by a mechanism of autoimmunity. We identified the chaperones that can putatively participate in molecular mimicry phenomena after SARS-CoV-2 infection, focusing on those for which endothelial cell plasma-cell membrane localization has already been demonstrated. We also postulate that post-translational modifications, induced by physical (shear) and chemical (metabolic) stress caused respectively by the risk factors hypertension and diabetes, might have a role in determining plasma-cell membrane localization and, in turn, autoimmune-induced endothelial damage.

Modification of DNA structure by reactive nitrogen species as a result of 2-methoxyestradiol-induced neuronal nitric oxide synthase uncoupling in metastatic osteosarcoma cells.

2-methoxyestradiol (2-ME) is a physiological anticancer compound, metabolite of 17ß-estradiol. Previously, our group evidenced that from mechanistic point of view one of anticancer mechanisms of action of 2-ME is specific induction and nuclear hijacking of neuronal nitric oxide synthase (nNOS), resulting in local generation of nitro-oxidative stress and finally, cancer cell death. The current study aims to establish the substantial mechanism of generation of reactive nitrogen species by 2-ME. We further achieved to identify the specific reactive nitrogen species involved in DNA-damaging mechanism of 2-ME. The study was performed using metastatic osteosarcoma 143B cells. We detected the release of biologically active (free) nitric oxide (•NO) with concurrent measurements of peroxynitrite (ONOO-) in real time in a single cell of 143B cell line by using •NO/ONOO- sensitive microsensors after stimulation with calcium ionophore. Detection of nitrogen dioxide (•NO2) and determination of chemical rate constants were carried out by a stopped-flow technique. The affinity of reactive nitrogen species toward the guanine base of DNA was evaluated by density functional theory calculations. Expression and localization of nuclear factor NF-kB was determined using imaging cytometry, while cell viability assay was evaluated by MTT assay. Herein, we presented that 2-ME triggers pro-apoptotic signalling cascade by increasing cellular reactive nitrogen species overproduction - a result of enzymatic uncoupling of increased nNOS protein levels. In particular, we proved that ONOO- and •NO2 directly formed from peroxynitrous acid (ONOOH) and/or by auto-oxidation of •NO, are inducers of DNA damage in anticancer mechanism of 2-ME. Specifically, the affinity of reactive nitrogen species toward the guanine base of DNA, evaluated by density functional theory calculations, decreased in the order: ONOOH > ONOO- > â€¢NO2 > â€¢NO. Therefore, we propose to consider the specific inducers of nNOS as an effective tool in the field of chemotherapy.

Design, development and validation of a system for automatic help to medical text understanding.

OBJECTIVE: The paper presents a web-based application, SIMPLE, that facilitates medical text comprehension by identifying the health-related terms of a medical text and providing the corresponding consumer terms and explanations. BACKGROUND: The comprehension of a medical text is often a difficult task for laypeople because it requires semantic abilities that can differ from a person to another, depending on his/her health-literacy level. Some systems have been developed for facilitating the comprehension of medical texts through text simplification, either syntactical or lexical. The ones dealing with lexical simplification usually replace the original text and do not provide additional information. We have developed a system that provides the consumer terms alongside the original medical terms and also adds consumer explanations. Moreover, differently from other solutions, our system works with multiple languages. METHODS: We have developed the SIMPLE application that is able to automatically: 1) identify medical terms in a medical text by using medical vocabularies; 2) translate the medical terms into consumer terms through medical-consumer thesauri; 3) provide term explanations by using health-consumer dictionaries. SIMPLE can be used as a standalone web application or can it be embedded into common health platforms for real time identification and explanation of medical terms. At present, it works with English and Italian texts but it can be easily extended to other languages. We have run subjective tests with both medical experts and non-experts as well as objective tests to verify the effectiveness of SIMPLE and its simplicity of use. RESULTS: Non-experts found SIMPLE easy to use and responsive. The big majority of respondents confirmed they were helped by SIMPLE in understanding medical texts and declared their willingness to continue using SIMPLE and to recommend it to other people. The subjective tests, conducted with medical experts on a set of Italian radiology reports, showed an agreement between SIMPLE and the experts, on the highlighted medical terms, that ranges between 74.05 % and 81.16 % as well as an agreement of around 60 % on the consumer term translation. The objective tests showed that the consumer terms, provided by SIMPLE, are, on average, eighteen times more familiar than the relative medical terms so proving, once more, the effectiveness of SIMPLE in simplifying the medical terms. CONCLUSIONS: The performed tests demonstrate the effectiveness of SIMPLE, its simplicity of use and the willingness of people in continuing with its use. SIMPLE provides, with a good agreement level, the same information that medical experts would provide. Finally, the consumer terms are 'objectively' more familiar than the related technical terms and as a consequence, much easier to understand.

Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM.

Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data. We have tested STREAM on several synthetic and real datasets generated with different single-cell technologies. We further demonstrate its utility for understanding myoblast differentiation and disentangling known heterogeneity in hematopoiesis for different organisms. STREAM is an open-source software package.

Deep learning architectures for prediction of nucleosome positioning from sequences data.

BACKGROUND: Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using a sequence features representation. RESULTS: In this work, we propose a deep learning model for nucleosome identification. Our model stacks convolutional layers and Long Short-term Memories to automatically extract features from short- and long-range dependencies in a sequence. Using this model we are able to avoid the feature extraction and selection steps while improving the classification performances. CONCLUSIONS: Results computed on eleven data sets of five different organisms, from Yeast to Human, show the superiority of the proposed method with respect to the state of the art recently presented in the literature.

Deep learning models for bacteria taxonomic classification of metagenomic data.

BACKGROUND: An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions. The above mentioned two sequencing technologies, SG and AMP, are used alternatively, for this reason in this work we propose a deep learning approach for taxonomic classification of metagenomic data, that can be employed for both of them. RESULTS: To test the proposed pipeline, we simulated both SG and AMP short-reads, from 1000 16S full-length sequences. Then, we adopted a k-mer representation to map sequences as vectors into a numerical space. Finally, we trained two different deep learning architecture, i.e., convolutional neural network (CNN) and deep belief network (DBN), obtaining a trained model for each taxon. We tested our proposed methodology to find the best parameters configuration, and we compared our results against the classification performances provided by a reference classifier for bacteria identification, known as RDP classifier. We outperformed the RDP classifier at each taxonomic level with both architectures. For instance, at the genus level, both CNN and DBN reached 91.3% of accuracy with AMP short-reads, whereas RDP classifier obtained 83.8% with the same data. CONCLUSIONS: In this work, we proposed a 16S short-read sequences classification technique based on k-mer representation and deep learning architecture, in which each taxon (from phylum to genus) generates a classification model. Experimental results confirm the proposed pipeline as a valid approach for classifying bacteria sequences; for this reason, our approach could be integrated into the most common tools for metagenomic analysis. According to obtained results, it can be successfully used for classifying both SG and AMP data.

Sperm DNA fragmentation: An early and reliable marker of air pollution.

Environmental factors could have a key role in the continuous and remarkable decline of sperm quality observed in the last decades. This study compared the seminal parameters and sperm DFI in men living in areas with different levels of air pollution. Results demonstrate that both steel plants workers and patients living in a high polluted area show a mean percentage of sperm DNA fragmentation above 30%, highlighting a clear sperm damage. In this work, two different techniques were used to measure sperm DNA damage in patients' groups, finding in both cases a high sperm DFI in patients living in polluted areas. We candidate sperm DNA fragmentation as a valuable early marker of the presence and harmful effects of pollution. We suggest that sperm DNA evaluation could be both an indicator of individual health and reproductive capacity, and a suitable datum to connect the surrounding environment with its effects.

Real-time detection of BRAF V600E mutation from archival hairy cell leukemia FFPE tissue by nanopore sequencing.

The MinION is a miniaturized high-throughput next generation sequencing platform of novel conception. The use of nucleic acids derived from formalin-fixed paraffin-embedded samples is highly desirable, but their adoption for molecular assays is hurdled by the high degree of fragmentation and by the chemical-induced mutations stemming from the fixation protocols. In order to investigate the suitability of MinION sequencing on formalin-fixed paraffin-embedded samples, the presence and frequency of BRAF c.1799T > A mutation was investigated in two archival tissue specimens of Hairy cell leukemia and Hairy cell leukemia Variant. Despite the poor quality of the starting DNA, BRAF mutation was successfully detected in the Hairy cell leukemia sample with around 50% of the reads obtained within 2 h of the sequencing start. Notably, the mutational burden of the Hairy cell leukemia sample as derived from nanopore sequencing proved to be comparable to a sensitive method for the detection of point mutations, namely the Digital PCR, using a validated assay. Nanopore sequencing can be adopted for targeted sequencing of genetic lesions on critical DNA samples such as those extracted from archival routine formalin-fixed paraffin-embedded samples. This result let speculating about the possibility that the nanopore sequencing could be trustably adopted for the real-time targeted sequencing of genetic lesions. Our report opens the window for the adoption of nanopore sequencing in molecular pathology for research and diagnostics.

2-Methoxyestradiol Affects Mitochondrial Biogenesis Pathway and Succinate Dehydrogenase Complex Flavoprotein Subunit A in Osteosarcoma Cancer Cells.

BACKGROUND/AIM: Dysregulation of mitochondrial pathways is implicated in several diseases, including cancer. Notably, mitochondrial respiration and mitochondrial biogenesis are favored in some invasive cancer cells, such as osteosarcoma. Hence, the aim of the current work was to investigate the effects of 2-methoxyestradiol (2-ME), a potent anticancer agent, on the mitochondrial biogenesis of osteosarcoma cells. MATERIALS AND METHODS: Highly metastatic osteosarcoma 143B cells were treated with 2-ME separately or in combination with L-lactate, or with the solvent (non-treated control cells). Protein levels of α-syntrophin and peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PGC-1α) were determined by western blotting. Impact of 2-ME on mitochondrial mass, regulation of cytochrome c oxidase I (COXI) expression, and succinate dehydrogenase complex flavoprotein subunit A (SDHA) was determined by immunofluorescence analyses. Inhibition of sirtuin 3 (SIRT3) activity by 2-ME was investigated by fluorescence assay and also, using molecular docking and molecular dynamics simulations. RESULTS: L-lactate induced mitochondrial biogenesis pathway via up-regulation of COXI. 2-ME inhibited mitochondrial biogenesis via regulation of PGC-1α, COXI, and SIRT3 in a concentration-dependent manner as a consequence of nuclear recruitment of neuronal nitric oxide synthase and nitric oxide generation. It was also proved that 2-ME inhibited SIRT3 activity by binding to both the canonical and allosteric inhibitor binding sites. Moreover, regardless of the mitochondrial biogenesis pathway, 2-ME affected the expression of SDHA. CONCLUSION: Herein, mitochondrial biogenesis pathway regulation and SDHA were presented as novel targets of 2-ME, and moreover, 2-ME was demonstrated as a potent inhibitor of SIRT3. L-lactate was confirmed to exert pro-carcinogenic effects on osteosarcoma cells via the induction of the mitochondrial biogenesis pathway. Thus, L-lactate level may be considered as a prognostic biomarker for osteosarcoma.

2-methoxyestradiol impacts on amino acids-mediated metabolic reprogramming in osteosarcoma cells by its interaction with NMDA receptor.

Deregulation of serine and glycine metabolism, have been identified to function as metabolic regulators in supporting tumor cell growth. The role of serine and glycine in regulation of cancer cell proliferation is complicated, dependent on concentrations of amino acids and tissue-specific. D-serine and glycine are coagonists of N-methyl-D-aspartate (NMDA) receptor subunit GRIN1. Importantly, NMDA receptors are widely expressed in cancer cells and play an important role in regulation of cell death, proliferation, and metabolism of numerous malignancies. The aim of the present work was to associate the metabolism of glycine and D-serine with the anticancer activity of 2-methoxyestradiol. 2-methoxyestradiol is a potent anticancer agent but also a physiological 17ß- estradiol metabolite. In the study we have chosen two malignant cell lines expressing functional NMDA receptors, that is osteosarcoma 143B and breast cancer MCF7. We used MTS assay, migration assay, flow cytometric analyses, Western blotting and immunoprecipitation techniques as well as molecular modeling studies. We have demonstrated the extensive crosstalk between the deregulated metabolic network and cancer cell signaling. Herein, we observed an anticancer effect of high concentrations of glycine and D-serine in osteosarcoma cells. In contrast, the amino acids when used at low, physiological concentrations induced the proliferation and migration of osteosarcoma cells. Importantly, the pro-cancergogenic effects of both glycine and D-serine where abrogated by the usage of 2-methoxyestradiol at both physiological and pharmacological relevant concentrations. The obtained data confirmed that 2-methoxyestradiol may be a physiological anticancer molecule.

Applications of alignment-free methods in epigenomics.

Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.

A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis.

BACKGROUND: Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from statistics to computer science. Following Handl et al., it can be summarized as a three step process: (1) choice of a distance function; (2) choice of a clustering algorithm; (3) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. RESULTS: A procedure is proposed for the assessment of the discriminative ability of a distance function. That is, the evaluation of the ability of a distance function to capture structure in a dataset. It is based on the introduction of a new external validation index, referred to as Balanced Misclassification Index (BMI, for short) and of a nontrivial modification of the well known Receiver Operating Curve (ROC, for short), which we refer to as Corrected ROC (CROC, for short). The main results are: (a) a quantitative and qualitative method to describe the intrinsic separation ability of a distance; (b) a quantitative method to assess the performance of a clustering algorithm in conjunction with the intrinsic separation ability of a distance function. The proposed procedure is more informative than the ones available in the literature due to the adopted tools. Indeed, the first one allows to map distances and clustering solutions as graphical objects on a plane, and gives information about the bias of the clustering algorithm with respect to a distance. The second tool is a new external validity index which shows similar performances with respect to the state of the art, but with more flexibility, allowing for a broader spectrum of applications. In fact, it allows not only to quantify the merit of each clustering solution but also to quantify the agglomerative or divisive errors due to the algorithm. CONCLUSIONS: The new methodology has been used to experimentally study three popular distance functions, namely, Euclidean distance d2, Pearson correlation dr and mutual information dMI. Based on the results of the experiments, we have that the Euclidean and Pearson correlation distances have a good intrinsic discrimination ability. Conversely, the mutual information distance does not seem to offer the same flexibility and versatility as the other two distances. Apparently, that is due to well known problems in its estimation. since it requires that a dataset must have a substantial number of features to be reliable. Nevertheless, taking into account such a fact, together with results presented in Priness et al., one receives an indication that dMI may be superior to the other distances considered in this study only in conjunction with clustering algorithms specifically designed for its use. In addition, it results that K-means, Average Link, and Complete link clustering algorithms are in most cases able to improve the discriminative ability of the distances considered in this study with respect to clustering. The methodology has a range of applicability that goes well beyond microarray data since it is independent of the nature of the input data. The only requirement is that the input data must have the same format of a "feature matrix". In particular it can be used to cluster ChIP-seq data.

A motif-independent metric for DNA sequence specificity.

BACKGROUND: Genome-wide mapping of protein-DNA interactions has been widely used to investigate biological functions of the genome. An important question is to what extent such interactions are regulated at the DNA sequence level. However, current investigation is hampered by the lack of computational methods for systematic evaluating sequence specificity. RESULTS: We present a simple, unbiased quantitative measure for DNA sequence specificity called the Motif Independent Measure (MIM). By analyzing both simulated and real experimental data, we found that the MIM measure can be used to detect sequence specificity independent of presence of transcription factor (TF) binding motifs. We also found that the level of specificity associated with H3K4me1 target sequences is highly cell-type specific and highest in embryonic stem (ES) cells. We predicted H3K4me1 target sequences by using the N- score model and found that the prediction accuracy is indeed high in ES cells.The software to compute the MIM is freely available at: https://github.com/lucapinello/mim. CONCLUSIONS: Our method provides a unified framework for quantifying DNA sequence specificity and serves as a guide for development of sequence-based prediction models.