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Mycobacterium tuberculosis can enter a dormant state enabling it to establish latent infections and to become tolerant to antibacterial drugs. Dormant bacteria’s physiology and the mechanism(s) used by bacteria to enter dormancy during infection are still unknown due to the lack of reliable animal models.

Motivation Recently, single-cell RNA-seq (scRNA-seq) data have been used to study cellular communication. Most bioinformatics methods infer only the intercellular signaling between groups of cells, mainly exploiting ligand–receptor expression levels. Only few methods consider the entire intercellular + intracellular signaling, mainly inferring lists/networks of signaling involved genes. Results Here, we present scSeqComm, a computational method to identify and quantify the evidence of ongoing intercellular and intracellular signaling from scRNA-seq data, and at the same time providing a functional characterization of the inferred cellular communication. The possibility to quantify the evidence of ongoing communication assists the prioritization of the results, while the combined evidence of both intercellular and intracellular signaling increase the reliability of inferred communication. The application to a scRNA-seq dataset of tumor microenvironment, the agreement with independent bioinformatics analysis, the validation using spatial transcriptomics data and the comparison with state-of-the-art intercellular scoring schemes confirmed the robustness and reliability of the proposed method. Availability and implementation scSeqComm R package is freely available at https://gitlab.com/sysbiobig/scseqcomm and https://sysbiobig.dei.unipd.it/software/#scSeqComm. Submitted software version and test data are available in Zenodo, at https://dx.doi.org/10.5281/zenodo.5833298. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Recently, several computational modeling approaches, such as agent-based models, have been applied to study the interaction dynamics between immune and tumor cells in human cancer. However, each tumor is characterized by a specific and unique tumor microenvironment, emphasizing the need for specialized and personalized studies of each cancer scenario. Results We present MAST, a hybrid Multi-Agent Spatio-Temporal model which can be informed using a data-driven approach to simulate unique tumor subtypes and tumor–immune dynamics starting from high-throughput sequencing data. It captures essential components of the tumor microenvironment by coupling a discrete agent-based model with a continuous partial differential equations-based model. The application to real data of human colorectal cancer tissue investigating the spatio-temporal evolution and emergent properties of four simulated human colorectal cancer subtypes, along with their agreement with current biological knowledge of tumors and clinical outcome endpoints in a patient cohort, endorse the validity of our approach. Availability and implementation MAST, implemented in Python language, is freely available with an open-source license through GitLab (https://gitlab.com/sysbiobig/mast), and a Docker image is provided to ease its deployment. The submitted software version and test data are available in Zenodo at https://dx.doi.org/10.5281/zenodo.7267745. Supplementary information Supplementary data are available at Bioinformatics Advances online.

The development of increasingly efficient and cost-effective high throughput DNA sequencing techniques has enhanced the possibility of studying complex microbial systems. Recently, researchers have shown great interest in studying the microorganisms that characterise different ecological niches. Differential abundance analysis aims to find the differences in the abundance of each taxa between two classes of subjects or samples, assigning a significance value to each comparison. Several bioinformatic methods have been specifically developed, taking into account the challenges of microbiome data, such as sparsity, the different sequencing depth constraint between samples and compositionality. Differential abundance analysis has led to important conclusions in different fields, from health to the environment. However, the lack of a known biological truth makes it difficult to validate the results obtained. In this work we exploit metaSPARSim, a microbial sequencing count data simulator, to simulate data with differential abundance features between experimental groups. We perform a complete comparison of recently developed and established methods on a common benchmark with great effort to the reliability of both the simulated scenarios and the evaluation metrics. The performance overview includes the investigation of numerous scenarios, studying the effect on methods’ results on the main covariates such as sample size, percentage of differentially abundant features, sequencing depth, feature variability, normalisation approach and ecological niches. Mainly, we find that methods show a good control of the type I error and, generally, also of the false discovery rate at high sample size, while recall seem to depend on the dataset and sample size.

Summary ITSoneWB (ITSone WorkBench) is a Galaxy-based bioinformatic environment where comprehensive and high-quality reference data are connected with established pipelines and new tools in an automated and easy-to-use service targeted at global taxonomic analysis of eukaryotic communities based on Internal Transcribed Spacer 1 variants high-throughput sequencing. Availability and implementation ITSoneWB has been deployed on the INFN-Bari ReCaS cloud facility and is freely available on the web at http://itsonewb.cloud.ba.infn.it/galaxy. Supplementary information Supplementary data are available at Bioinformatics online.

Chronic Myeloid Leukemia is a clonal disorder characterized by the presence of the Ph-chromosome and the BCR-ABL tyrosine-kinase (TK). Target-therapy with Imatinib has greatly improved its outcome. Deeper and faster responses are reported with the second-generation TKI Nilotinib. Sustained responses may enable TKI discontinuation. However, even in a complete molecular response, some patients experience disease recurrence possibly due to persistence of quiescent leukemic CD34+/lin−Ph+ stem cells (LSCs). Degree and mechanisms of LSCs clearance during TKI treatment are not clearly established. The PhilosoPhi34 study was designed to verify the in-vivo activity and timecourse of first-line Nilotinib therapy on BM CD34+/lin−Ph+ cells clearance. Eighty-seven CP-CML patients were enrolled. BM cells were collected and tested for Ph+ residual cells, at diagnosis, 3, 6 and 12 months of treatment. FISH analysis of unstimulated CD34+/lin− cells in CCyR patients were positive in 8/65 (12.3%), 5/71 (7%), 0/69 (0%) evaluable tests, respectively. Per-Protocol analysis response rates were as follows: CCyR 95% at 12 months, MR4.5 31% and 46% at 12 and 36 months, respectively. An exploratory Gene Expression Profiling (GEP) study of CD34+/lin− cells was performed on 30 patients at diagnosis and after, on 79 patients at diagnosis vs 12 months of nilotinib treatment vs 10 healthy subjects. Data demonstrated some genes significantly different expressed: NFKBIA, many cell cycle genes, ABC transporters, JAK-STAT signaling pathway (JAK2). In addition, a correlation between different expression of some genes (JAK2, OLFM4, ICAM1, NFKBIA) among patients at diagnosis and their achievement of an early and deeper MR was observed.

Background 16S rRNA-gene sequencing is a valuable approach to characterize the taxonomic content of the whole bacterial population inhabiting a metabolic and spatial niche, providing an important opportunity to study bacteria and their role in many health and environmental mechanisms. The analysis of data produced by amplicon sequencing, however, brings very specific methodological issues that need to be properly addressed to obtain reliable biological conclusions. Among these, 16S count data tend to be very sparse, with many null values reflecting species that are present but got unobserved due to the multiplexing constraints. However, current data workflows do not consider a step in which the information about unobserved species is recovered. Results In this work, we evaluate for the first time the effects of introducing in the 16S data workflow a new preprocessing step, zero-imputation, to recover this lost information. Due to the lack of published zero-imputation methods specifically designed for 16S count data, we considered a set of zero-imputation strategies available for other frameworks, and benchmarked them using in silico 16S count data reflecting different experimental designs. Additionally, we assessed the effect of combining zero-imputation and normalization, i.e. the only preprocessing step in current 16S workflow. Overall, we benchmarked 35 16S preprocessing pipelines assessing their ability to handle data sparsity, identify species presence/absence, recovery sample proportional abundance distributions, and improve typical downstream analyses such as computation of alpha and beta diversity indices and differential abundance analysis. Conclusions The results clearly show that 16S data analysis greatly benefits from a properly-performed zero-imputation step, despite the choice of the right zero-imputation method having a pivotal role. In addition, we identify a set of best-performing pipelines that could be a valuable indication for data analysts.

In the current research landscape, microbiota composition studies are of extreme interest, since it has been widely shown that resident microorganisms affect and shape the ecological niche they inhabit. This complex micro-world is characterized by different types of interactions. Understanding these relationships provides a useful tool for decoding the causes and effects of communities’ organizations. Next-Generation Sequencing technologies allow to reconstruct the internal composition of the whole microbial community present in a sample. Sequencing data can then be investigated through statistical and computational method coming from network theory to infer the network of interactions among microbial species. : Since there are several network inference approaches in the literature, in this paper we tried to shed light on their main characteristics and challenges, providing a useful tool not only to those interested in using the methods, but also to those who want to develop new ones. In addition, we focused on the frameworks used to produce synthetic data, starting from the simulation of network structures up to their integration with abundance models, with the aim of clarifying the key points of the entire generative process.

Background Targeted amplicon sequencing of the 16S ribosomal RNA gene is one of the key tools for studying microbial diversity. The accuracy of this approach strongly depends on the choice of primer pairs and, in particular, on the balance between efficiency, specificity and sensitivity in the amplification of the different bacterial 16S sequences contained in a sample. There is thus the need for computational methods to design optimal bacterial 16S primers able to take into account the knowledge provided by the new sequencing technologies. Results We propose here a computational method for optimizing the choice of primer sets, based on multi-objective optimization, which simultaneously: 1) maximizes efficiency and specificity of target amplification; 2) maximizes the number of different bacterial 16S sequences matched by at least one primer; 3) minimizes the differences in the number of primers matching each bacterial 16S sequence. Our algorithm can be applied to any desired amplicon length without affecting computational performance. The source code of the developed algorithm is released as the mopo16S software tool (Multi-Objective Primer Optimization for 16S experiments) under the GNU General Public License and is available at http://sysbiobig.dei.unipd.it/?q=Software#mopo16S. Conclusions Results show that our strategy is able to find better primer pairs than the ones available in the literature according to all three optimization criteria. We also experimentally validated three of the primer pairs identified by our method on multiple bacterial species, belonging to different genera and phyla. Results confirm the predicted efficiency and the ability to maximize the number of different bacterial 16S sequences matched by primers. Electronic supplementary material The online version of this article (10.1186/s12859-018-2360-6) contains supplementary material, which is available to authorized users.

Background Though Illumina has largely dominated the RNA-Seq field, the simultaneous availability of Ion Torrent has left scientists wondering which platform is most effective for differential gene expression (DGE) analysis. Previous investigations of this question have typically used reference samples derived from cell lines and brain tissue, and do not involve biological variability. While these comparisons might inform studies of tissue-specific expression, marked by large-scale transcriptional differences, this is not the common use case. Results Here we employ a standard treatment/control experimental design, which enables us to evaluate these platforms in the context of the expression differences common in differential gene expression experiments. Specifically, we assessed the hepatic inflammatory response of mice by assaying liver RNA from control and IL-1β treated animals with both the Illumina HiSeq and the Ion Torrent Proton sequencing platforms. We found the greatest difference between the platforms at the level of read alignment, a moderate level of concordance at the level of DGE analysis, and nearly identical results at the level of differentially affected pathways. Interestingly, we also observed a strong interaction between sequencing platform and choice of aligner. By aligning both real and simulated Illumina and Ion Torrent data with the twelve most commonly-cited aligners in the literature, we observed that different aligner and platform combinations were better suited to probing different genomic features; for example, disentangling the source of expression in gene-pseudogene pairs. Conclusions Taken together, our results indicate that while Illumina and Ion Torrent have similar capacities to detect changes in biology from a treatment/control experiment, these platforms may be tailored to interrogate different transcriptional phenomena through careful selection of alignment software. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-4011-0) contains supplementary material, which is available to authorized users.

Background In the last few years, 16S rRNA gene sequencing (16S rDNA-seq) has seen a surprisingly rapid increase in election rate as a methodology to perform microbial community studies. Despite the considerable popularity of this technique, an exiguous number of specific tools are currently available for proper 16S rDNA-seq count data preprocessing and simulation. Indeed, the great majority of tools have been developed adapting methodologies previously used for bulk RNA-seq data, with poor assessment of their applicability in the metagenomics field. For such tools and the few ones specifically developed for 16S rDNA-seq data, performance assessment is challenging, mainly due to the complex nature of the data and the lack of realistic simulation models. In fact, to the best of our knowledge, no software thought for data simulation are available to directly obtain synthetic 16S rDNA-seq count tables that properly model heavy sparsity and compositionality typical of these data. Results In this paper we present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution in order to realistically simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity. It provides ready-to-use count matrices and comes with the possibility to reproduce different pre-coded scenarios and to estimate simulation parameters from real experimental data. The tool is made available at http://sysbiobig.dei.unipd.it/?q=Software#metaSPARSimand https://gitlab.com/sysbiobig/metasparsim. Conclusion metaSPARSim is able to generate count matrices resembling real 16S rDNA-seq data. The availability of count data simulators is extremely valuable both for methods developers, for which a ground truth for tools validation is needed, and for users who want to assess state of the art analysis tools for choosing the most accurate one. Thus, we believe that metaSPARSim is a valuable tool for researchers involved in developing, testing and using robust and reliable data analysis methods in the context of 16S rRNA gene sequencing.

Chronic myeloid leukemia (CML) is characterized by the constitutive tyrosine kinase activity of the oncoprotein BCR-ABL1 in myeloid progenitor cells that activates multiple signal transduction pathways leading to the leukemic phenotype. The tyrosine-kinase inhibitor (TKI) nilotinib inhibits the tyrosine kinase activity of BCR-ABL1 in CML patients. Despite the success of nilotinib treatment in patients with chronic-phase (CP) CML, a population of Philadelphia-positive (Ph+) quiescent stem cells escapes the drug activity and can lead to drug resistance. The molecular mechanism by which these quiescent cells remain insensitive is poorly understood. The aim of this study was to compare the gene expression profiling (GEP) of bone marrow (BM) CD34+/lin- cells from CP-CML patients at diagnosis and after 12 months of nilotinib treatment by microarray, in order to identify gene expression changes and the dysregulation of pathways due to nilotinib action. We selected BM CD34+/lin- cells from 78 CP-CML patients at diagnosis and after 12 months of first-line nilotinib therapy and microarray analysis was performed. GEP bioinformatic analyses identified 2,959 differently expressed probes and functional clustering determined some significantly enriched pathways between diagnosis and 12 months of nilotinib treatment. Among these pathways, we observed the under expression of 26 genes encoding proteins belonging to the cell cycle after 12 months of nilotinib treatment which led to the up-regulation of chromosome replication, cell proliferation, DNA replication, and DNA damage checkpoint at diagnosis. We demonstrated the under expression of the ATP-binding cassette (ABC) transporters ABCC4, ABCC5, and ABCD3 encoding proteins which pumped drugs out of the cells after 12 months of nilotinib. Moreover, GEP data demonstrated the deregulation of genes involved in the JAK-STAT signaling pathway. The down-regulation of JAK2, IL7, STAM, PIK3CA, PTPN11, RAF1, and SOS1 key genes after 12 months of nilotinib could demonstrate the up-regulation of cell cycle, proliferation and differentiation via MAPK and PI3K-AKT signaling pathways at diagnosis.

MotivationSingle cell RNA-seq (scRNA-seq) count data show many differences compared with bulk RNA-seq count data, making the application of many RNA-seq pre-processing/analysis methods not straightforward or even inappropriate. For this reason, the development of new methods for handling scRNA-seq count data is currently one of the most active research fields in bioinformatics. To help the development of such new methods, the availability of simulated data could play a pivotal role. However, only few scRNA-seq count data simulators are available, often showing poor or not demonstrated similarity with real data.ResultsIn this article we present SPARSim, a scRNA-seq count data simulator based on a Gamma-Multivariate Hypergeometric model. We demonstrate that SPARSim allows to generate count data that resemble real data in terms of count intensity, variability and sparsity, performing comparably or better than one of the most used scRNA-seq simulator, Splat. In particular, SPARSim simulated count matrices well resemble the distribution of zeros across different expression intensities observed in real count data.Availability and implementationSPARSim R package is freely available at http://sysbiobig.dei.unipd.it/? q=SPARSim and at https://gitlab.com/sysbiobig/sparsim.Supplementary informationSupplementary data are available at Bioinformatics online.

In the PhilosoPhi34 study (EudraCT: 2012-005062-34) on 87 CP-CML patients, we analyzed the gene expression profiling (GEP) of bone marrow (BM) CD34+/lin- cells of 79 patients with chronic-phase chronic myeloid leukemia (CP-CML) patients at diagnosis and after 12 months of nilotinib treatment (Pungolino et al. AM J Hematol. 2018). FISH analyses identified CD34+/lin- Ph+ cells in all 79 CML-CP patients at diagnosis. 78/79 patients achieved at least a complete cytogenetic response after 12 months of nilotinib whereas 1/79 patients relapsed at 12 months. No Ph+ nuclei were detected in 78/79 patients at 12 months. We have demonstrated that genes involved in the JAK-STAT signaling pathway, the cell cycle, and the ATP-binding cassette (ABC) transporters were significantly over expressed in patients at diagnosis compared to 12 months of nilotinib treatment (Trojani et al, PLoS One. 2019). In this preliminary study, we isolated BM CD34+/lin- cells from 9 healthy donors (CTRLs). We investigated the gene expression profiling of 79 CP-CML patients at diagnosis vs. the same patients after 12 months of nilotinib treatment (12 months) vs. 9 CTRLs using Affymetrix HTA 2.0. Data was preprocessed and normalized using RMA and ComBat. Selection of differentially expressed genes (DEg) was performed at diagnosis vs. 12 months of nilotinib vs. CTRLs, using Statistical Analysis for Microarrays (SAM) on 3 groups and a Benjamini Hochberg false discovey rate threshold of 5%, followed, for significance comparisons, by a pair-wise SAM test. We focused on 34 genes of the cell cycle and mitosis, 6 genes belonging to the JAK-STAT signaling pathway, and the ABC transporter gene ABCD3 which were significantly under expressed at diagnosis vs. 12 months of nilotinib vs. CRTLs (Tab.1). In the cell cycle, we found that ORC2, ORC4, ORC5, MCM6, and HDAC2 (G1 phase) were progressively significantly down regulated at diagnosis vs. 12 months vs. CTRLs. We noticed HDAC2 which showed the high fold changes of 2.89 and 4.29 in the comparison between 12 months vs. CTRLs and between diagnosis vs. CTRLs, respectively. This gene plays a crucial role in CML, as HDAC inhibitors treatment represent an effective strategy to target leukemic stem cells in CP-CML patients receiving tyrosine kinase inhibitors. CCNA2, CDK7, CDC6, DBF4 (S phase), WEE1, PRKDC, ATM, MDM2, CCNB1 (G2 phase), and TTK, MAD2L1, BUB1, BUB3, ANAPC4, CDC27, SMC3, YWHAE, and YWHAZ (M phase) were progressively down regulated at diagnosis vs. 12 months vs. CTRLs. Among them, SMC3, YWHAE and YWHAZ showed the following high fold changes in the comparison between 12 months vs. CTRLs and between diagnosis vs. CTRLs: 2.31 and 3.15, 2.59 and 3.94, 2.75 and 4.15, respectively. Notably, the proto-oncogene MDM2 which promotes the development of tumors by targeting p53, was progressively up regulated in CTRLs vs. 12 months vs. diagnosis. In the mitosis, we detected that 10 genes playing a crucial role in mitotic chromosome organization, were progressively under expressed at diagnosis vs. 12 months vs. CTRLs (Tab.1). Notably, CLAPS2, ZW10 and ANLN (hematopoietic cell differentiation) regulate the exit from mitosis. NDC80, SMC4, ZNF207, and NEK2 take part in the mitotic sister chromatid segregation whereas CENPE and SMC2 are mitotic cell cycle check points. In the JAK-STAT signaling pathway, SOS1, PIK3CA, IL7, JAK2, STAM, and PTPN11 were progressively up regulated in CTRLs vs. 12 months vs. diagnosis (Tab.1). ABCD3, encoding a protein which pumped drugs out of the cells, was progressively under expressed at diagnosis vs. 12 months vs. CRTLs as shown in Tab.1. In conclusion, we found progressive gene expression changes in BM CD34+/lin- cells of 79 CP-CML patients at diagnosis vs. 12 months of nilotinib vs. the normal cell counterparts of 9 CTRLs in the cell cycle, JAK-STAT, and the ABC transporter ABCD3. FISH analyses demonstrated that the BM CD34+/lin- cells of 78/79 patients after 12 months of nilotinib were Ph-negative. Despite, our GEP results suggested that BM CD34+/lin- cells after 12 months of nilotinib treatment and the normal cell counterparts differed mostly in the expression of genes regulating the cell cycle and the JAK-STAT signaling pathway. Disclosures Rossi: Daiichi-Sankyo: Consultancy; Roche: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Jazz: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria; Mundipharma: Honoraria; BMS: Honoraria; Sandoz: Honoraria. Elena:Novartis: Consultancy; Pfizer: Consultancy. Iurlo:Pfizer: Other: Speaker Honoraria; Incyte: Other: Speaker Honoraria; Novartis: Other: Speaker Honoraria.

Introduction Chronic myeloid leukemia (CML) is a stem cell disease characterized by the constitutive activity of the oncoprotein BCR-ABL that activates multiple signal transduction pathways. Tyrosine-kinase inhibitor (TKI) nilotinib successfully inhibits the activation and the proliferative function of BCR-ABL in patients with CP-CML. Despite the success of nilotinib, some patients become refractory suggesting the presence of a population of Philadelphia positive (Ph+) quiescent stem cells escaping the drug activity. Thus, the molecular mechanisms underlying CML remain poorly understood. In this study, we enrolled 87 CP-CML patients (Pungolino et. al. Am J Hematol. 2018). Samples were collected on the behalf of the Rete Ematologica Lombarda (REL) the PhilosoPhi34 study (EudraCT: 2012-005062-34), which included 15 centers from Italy. We undertook gene expression profiling (GEP) of selected bone marrow (BM) CD34+/lin- cells of 80 patients at diagnosis vs. the same patients after 12 months of nilotinib to investigate gene expression changes induced by the treatment. Methods We isolated CD34+/lin- cells from BM samples in 87 patients at diagnosis whereas the same cells were also selected from 80/87 patients after 3, 6 and 12 months of nilotinib (Trojani et. al. Cancer Biomark. 2017). BM mononuclear cells (MNCs) as well as BM CD34+/lin- cells of all 80 CML patients were counted at diagnosis and during the treatment with nilotinib (at 3, 6, and 12 months, respectively). Standard FISH tested isolated BM CD34/lin- cells for the 87 patients at diagnosis, and for 80/87 patients after 3, 6 and 12 months of nilotinib treatment, respectively. Therefore, we performed GEP analyses of selected BM CD34+/lin- cells of 80/87 patients at diagnosis vs. the same patients after 12 months of nilotinib treatment. Then, we executed bioinformatic preprocessing and correction for batch effects on raw microarray data. Finally, we conducted differential expression analysis and significantly perturbed genes were subjected to functional clustering. Results We observed a wide variability of the number of BM MNCs as well as the number of the BM CD34+/lin- cells among the 80 CP-CML patients at diagnosis and after 3, 6 and 12 months of nilotinib for each patient (Table 1). Figure 1 showed that the number of the BM CD34+/lin- cells dramatically decreased between the diagnosis and after 3 as well as 6 months of nilotinib. We noticed that the BM CD34+/lin- cells slightly increased between 6 and 12 months of nilotinib which might be caused by the gradual repopulation of the normal CD34+/lin- cells in the bone marrow as FISH results suggested. FISH analysis detected CD34+/lin- Ph+ cells in 87 CP-CML patients at diagnosis. No positive Ph+ nuclei were detected on CD34+/lin- cells of 79/80 patients after 12 months of treatment (to categorize a sample as negative, at least 200 nuclei were examined). All of these 79 patients achieved at least complete cytogenetic response. 1/80 patient relapsed at 12 months. We conducted GEP analyses on 78 subjects because, due to experimental issues, two patients were not considered for differential expression analyses, as the microarray CEL files of the 12 months' samples were corrupted and missed probe intensities for most of the probes. GEP analyses determined 2,959 significantly differently expressed probes between diagnosis and after 12 months of nilotinib treatment. Functional clustering identified some pathways significantly enriched between diagnosis and 12 months of nilotinib. Among these pathways, we found that ABCC4, ABCC5, and ABCD3 genes associated with ATP-binding cassette (ABC) transporters were up regulated at diagnosis. GEP results highlighted that 26 genes belonging to cell cycle, mitosis, DNA damage and repair were over expressed at diagnosis. Moreover, GEP data demonstrated that JAK-STAT signaling pathway was deregulated: JAK2, IL7, STAM, PIK3CA, PTPN11, RAF1, SOS1 were over expressed whereas IL22RA1 was under expressed at diagnosis vs. 12 months of nilotinib, respectively. Conclusions In summary, we reported that BM CD34+/lin- cells from CP-CML patients after 12 months of nilotinib were characterized by changes of expression of genes involved in cell cycle checkpoints and mitosis, ABC transporters genes that pump drugs outside form the cells, and JAK-STAT signaling pathway genes responsible for the proliferation, differentiation and cell survival in CML. Disclosures Rossi: Teva: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Jazz: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria; Mundipharma: Honoraria; BMS: Honoraria; Sandoz: Honoraria; Gilead: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees.

The sequencing of the transcriptomes of single-cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types and for the study of stochastic gene expression. In recent years, various tools for analyzing single-cell RNA-sequencing data have been proposed, many of them with the purpose of performing differentially expression analysis. In this work, we compare four different tools for single-cell RNA-sequencing differential expression, together with two popular methods originally developed for the analysis of bulk RNA-sequencing data, but largely applied to single-cell data. We discuss results obtained on two real and one synthetic dataset, along with considerations about the perspectives of single-cell differential expression analysis. In particular, we explore the methods performance in four different scenarios, mimicking different unimodal or bimodal distributions of the data, as characteristic of single-cell transcriptomics. We observed marked differences between the selected methods in terms of precision and recall, the number of detected differentially expressed genes and the overall performance. Globally, the results obtained in our study suggest that is difficult to identify a best performing tool and that efforts are needed to improve the methodologies for single-cell RNA-sequencing data analysis and gain better accuracy of results.

Alignment is the first step in most RNA-seq analysis pipelines, and the accuracy of downstream analyses depends heavily on it. Unlike most steps in the pipeline, alignment is particularly amenable to benchmarking with simulated data. We performed a comprehensive benchmarking of 14 common splice-aware aligners for base, read, and exon junction-level accuracy and compared default with optimized parameters. We found that performance varied by genome complexity, and accuracy and popularity were poorly correlated. The most widely cited tool underperforms for most metrics, particularly when using default settings.