Clinical evaluation of a low-coverage whole-genome test for detecting homologous recombination deficiency in ovarian cancer.

BACKGROUND: The PAOLA-1/ENGOT-ov25 trial showed that maintenance olaparib plus bevacizumab increases survival of advanced ovarian cancer patients with homologous recombination deficiency (HRD). However, decentralized solutions to test for HRD in clinical routine are scarce. The goal of this study was to retrospectively validate on tumor samples from the PAOLA-1 trial, the decentralized SeqOne assay, which relies on shallow Whole Genome Sequencing (sWGS) to capture genomic instability and targeted sequencing to determine BRCA status. METHODS: The study comprised 368 patients from the PAOLA-1 trial. The SeqOne assay was compared to the Myriad MyChoice HRD test (Myriad Genetics), and results were analyzed with respect to Progression-Free Survival (PFS). RESULTS: We found a 95% concordance between the HRD status of the two tests (95% Confidence Interval (CI); 92%-97%). The Positive Percentage Agreement (PPA) of the sWGS test was 95% (95% CI; 91%-97%) like its Negative Percentage Agreement (NPA) (95% CI; 89%-98%). In patients with HRD-positive tumors treated with olaparib plus bevacizumab, the PFS Hazard Ratio (HR) was 0.38 (95% CI; 0.26-0.54) with SeqOne assay and 0.32 (95% CI; 0.22-0.45) with the Myriad assay. In patients with HRD-negative tumors, HR was 0.99 (95% CI; 0.68-1.42) and 1.05 (95% CI; 0.70-1.57) with SeqOne and Myriad assays. Among patients with BRCA-wildtype tumors, those with HRD-positive tumors, benefited from olaparib plus bevacizumab maintenance, with HR of 0.48 (95% CI: 0.29-0.79) and of 0.38 (95% CI: 0.23 to 0.63) with the SeqOne and Myriad assay. CONCLUSION: The SeqOne assay offers a clinically validated approach to detect HRD.

Numerical simulations of high viscosity DNAPL recovery in highly permeable porous media under isothermal and non-isothermal conditions.

We developed a decimetric size model based on coupling generalized Darcy's law and heat-transfer equations to model viscous dense non-aqueous phase liquid (DNAPL) pumping through highly permeable porous media under non-isothermal conditions. The presence of fingering and non-wetting phase ganglia was modeled through an unsteady capillary diffusion coefficient and an arbitrary heterogeneous permeability field. The model was validated using existing experimental data of a simple case, an oil injection in a 2D tank packed with glass beads. Next, we compared the results of this model against a DNAPL extracting situation in the 2D tank to better understand the two-phase flow behavior in highly permeable porous media. We found that natural convection during heating plays an essential role in heat transfer, especially in the wetting phase zone. By adding the dynamic effect (unsteady conditions) we were better able to describe the presence of the ganglia in porous media. We observed good agreement between modeled and experimental oil saturation curves until the breakthrough point, with a mean relative error of about 10% for low and high flow rates, and 8% and 16% after breakthrough for low and high flow rates, respectively. Extracting viscous oil at low flow rates and high temperature generates less fingering and is well described by the generalized Darcy's law. The remobilization of residual non-wetting ganglia after the breakthrough point at the outlet is, however, difficult to simulate using the generalized Darcy's law. In the end, we treated this issue by using a perturbed permeability field to simulate the observed fingering in the 2D tank.

Predominance of BRCA2 Mutation and Estrogen Receptor Positivity in Unselected Breast Cancer with BRCA1 or BRCA2 Mutation.

BACKGROUND: Poly(ADP-ribose) polymerase 1 inhibitor (PARPi) agents can improve progression-free survival of patients with breast cancer who carry a germline BRCA1 or BRCA2 pathogenic or likely pathogenic variant (gBRCA) in both the metastatic and adjuvant setting. Therefore, we need to reassess the frequency of gBRCA1 and gBRCA2 in order to redefine the criteria for women and tumor phenotype that should be tested. OBJECTIVE: We studied the relative distribution of gBRCA1 and gBRCA2 in unselected populations of women with breast cancer and in unaffected individuals. We also analyzed the proportion of estrogen receptor (ER)-positive (ER+) tumors in unselected breast cancer patients with gBRCA. DESIGN: We performed a meta-analysis of studies of unselected breast cancer that analyzed the relative contribution of gBRCA1 versus gBRCA2 among unselected breast cancer cases in gBRCA carriers. We then performed a meta-analysis of gBRCA carriage in unaffected individuals from genome-wide population studies, the gnomAD databank, and case-control studies. RESULTS: The BRCA2 gene was involved in 54% of breast cancer cases in unselected patients with gBRCA (n = 108,699) and 60% of unaffected individuals (n = 238,973) as compared with 38% of the largest gBRCA family cohort (n = 29,700). The meta-analysis showed that 1.66% (95% CI 1.08-2.54) and 1.71% (95% CI 1.33-2.2) of unselected breast cancer patients carried gBRCA1 and gBRCA2, respectively. In a population of unaffected individuals, the frequency of heterozygosity for gBRCA1 and gBRCA2 was estimated at 1/434 and 1/288, respectively. Nearly 0.5% of unaffected individuals in the studied populations carried a gBRCA. Carriage of a gBRCA was 2.5% for patients with ER+ tumors (95% CI 1.5-4.1) and 5.7% (95% CI 5.1-6.2) for those with ER- tumors. Overall, 58% of breast tumors occurring in women carrying a gBRCA were ER+ (n = 86,870). CONCLUSIONS: This meta-analysis showed that gBRCA2 carriage is predominant in unselected breast cancer patients and unaffected individuals. ER+ tumors among women with gBRCA-related breast cancer are predominant and have been underestimated. Because PARPi agents improve progression-free survival with ER+ gBRCA breast cancer in most clinical trials, breast cancer should be considered, regardless of ER status, for BRCA1/2 screening for therapeutic purposes.

Genome Alert!: A standardized procedure for genomic variant reinterpretation and automated gene-phenotype reassessment in clinical routine.

PURPOSE: Retrospective interpretation of sequenced data in light of the current literature is a major concern of the field. Such reinterpretation is manual and both human resources and variable operating procedures are the main bottlenecks. METHODS: Genome Alert! method automatically reports changes with potential clinical significance in variant classification between releases of the ClinVar database. Using ClinVar submissions across time, this method assigns validity category to gene-disease associations. RESULTS: Between July 2017 and December 2019, the retrospective analysis of ClinVar submissions revealed a monthly median of 1247 changes in variant classification with potential clinical significance and 23 new gene-disease associations. Re-examination of 4929 targeted sequencing files highlighted 45 changes in variant classification, and of these classifications, 89% were expert validated, leading to 4 additional diagnoses. Genome Alert! gene-disease association catalog provided 75 high-confidence associations not available in the OMIM morbid list; of which, 20% became available in OMIM morbid list For more than 356 negative exome sequencing data that were reannotated for variants in these 75 genes, this elective approach led to a new diagnosis. CONCLUSION: Genome Alert! (https://genomealert.univ-grenoble-alpes.fr/) enables systematic and reproducible reinterpretation of acquired sequencing data in a clinical routine with limited human resource effect.

Experimental study of thermally enhanced recovery of high-viscosity DNAPL in saturated porous media under non-isothermal conditions.

Thermal enhancement is known to be an efficient way to decrease the residual saturation of some common dense non-aqueous phase liquids (DNAPLs) after pumping. However, the effect of transient heat transfer during the recovery of a high-viscosity contaminant, such as coal tar, in highly permeable porous media is still unknown. A 2D tank experimental setup allowing monitoring of temperature and saturation fields during DNAPL pumping has been developed. Experiments were run under isothermal and non-isothermal conditions, at low and high flow rates. We investigated the presence of viscous fingering and how that influences the shape of the cone of depression, as well as the residual saturation. The saturation fields show that less viscous fingering occurs in pre-heated cases and that heating increases the recovery efficiency. Increasing the temperature increases the critical velocity and the viscosity ratio and helps to stabilize the interface between the non-wetting and wetting phase. Observations were first made on an oil and ethanol fluid pair because its properties were known, before extending the experiments to a coal tar and water fluid pair. Residual oil saturation after pumping was decreased by 6-16% in all pre-heated conditions. Pumping at low flow rate in these conditions leaves the smallest oil residual saturation (20%) after pumping. A low flow rate increases the recovery efficiency by reducing viscous fingering and by spreading the generated heat to a larger part of the tank. Finally, results on coal tar pumping show that the high thermal conductivity of water helps in keeping the temperature high during pumping. The residual coal tar saturation was reduced from 40% at 20 °C to 28% when pre-heating the tank. Operating at a low flow rate and with a uniform temperature is the key to recovering the highest amount of a viscous DNAPL such as coal tar from the soil and satisfying cleanup goals when using thermally enhanced pumping.

Two new convenient syntheses of 14C-squalene from turbinaric acid.

Carbon-14 labeled (6E,10E,14E,18E)-2,6,10,15,19,23-hexamethyltetracosa-2,6,10,14,18,22-hexaene, also known as squalene, was synthesized as a tool for pharmacokinetic studies. Two simple and efficient labeling approaches were developed to give [2-14 C]-squalene and [3-14 C]-squalene from a halogenated precursor derived from turbinaric acid. They were obtained in 13.5% radiochemical yield in 6 steps and in 38% radiochemical yield in 3 steps respectively from carbon-14 labeled potassium cyanide with a radiochemical purity higher than 98% in both cases.

Cabazitaxel is more active than first-generation taxanes in ABCB1(+) cell lines due to its reduced affinity for P-glycoprotein.

PURPOSE: The primary aim of this study was to determine cabazitaxel's affinity for the ABCB1/P-glycoprotein (P-gp) transporter compared to first-generation taxanes. METHODS: We determined the kinetics of drug accumulation and retention using [14C]-labeled taxanes in multidrug-resistant (MDR) cells. In addition, membrane-enriched fractions isolated from doxorubicin-selected MES-SA/Dx5 cells were used to determine sodium orthovanadate-sensitive ATPase stimulation after exposure to taxanes. Custom [3H]-azido-taxane analogues were synthesized for the photoaffinity labeling of P-gp. RESULTS: The maximum intracellular drug concentration was achieved faster with [14C]-cabazitaxel (5 min) than [14C]-docetaxel (15-30 min). MDR cells accumulated twice as much cabazitaxel than docetaxel, and these levels could be restored to parental levels in the presence of the P-gp inhibitor PSC-833 (valspodar). Efflux in drug-free medium confirmed that MDR cells retained twice as much cabazitaxel than docetaxel. There was a strong association (r2 = 0.91) between the degree of taxane resistance conferred by P-gp expression and the accumulation differences observed with the two taxanes. One cell model expressing low levels of P-gp was not cross-resistant to cabazitaxel while demonstrating modest resistance to docetaxel. Furthermore, there was a 1.9 × reduction in sodium orthovanadate-sensitive ATPase stimulation resulting from treatment with cabazitaxel compared to docetaxel. We calculated a dissociation constant (Kd) value of 1.7 µM for [3H]-azido-docetaxel and ~ 7.5 µM for [3H]-azido-cabazitaxel resulting in a 4.4 × difference in P-gp labeling, and cold docetaxel was a more effective competitor than cabazitaxel. CONCLUSION: Our studies confirm that cabazitaxel is more active in ABCB1(+) cell models due to its reduced affinity for P-gp compared to docetaxel.

DE-kupl: exhaustive capture of biological variation in RNA-seq data through k-mer decomposition.

We introduce a k-mer-based computational protocol, DE-kupl, for capturing local RNA variation in a set of RNA-seq libraries, independently of a reference genome or transcriptome. DE-kupl extracts all k-mers with differential abundance directly from the raw data files. This enables the retrieval of virtually all variation present in an RNA-seq data set. This variation is subsequently assigned to biological events or entities such as differential long non-coding RNAs, splice and polyadenylation variants, introns, repeats, editing or mutation events, and exogenous RNA. Applying DE-kupl to human RNA-seq data sets identified multiple types of novel events, reproducibly across independent RNA-seq experiments.

SimBA: A methodology and tools for evaluating the performance of RNA-Seq bioinformatic pipelines.

BACKGROUND: The evolution of next-generation sequencing (NGS) technologies has led to increased focus on RNA-Seq. Many bioinformatic tools have been developed for RNA-Seq analysis, each with unique performance characteristics and configuration parameters. Users face an increasingly complex task in understanding which bioinformatic tools are best for their specific needs and how they should be configured. In order to provide some answers to these questions, we investigate the performance of leading bioinformatic tools designed for RNA-Seq analysis and propose a methodology for systematic evaluation and comparison of performance to help users make well informed choices. RESULTS: To evaluate RNA-Seq pipelines, we developed a suite of two benchmarking tools. SimCT generates simulated datasets that get as close as possible to specific real biological conditions accompanied by the list of genomic incidents and mutations that have been inserted. BenchCT then compares the output of any bioinformatics pipeline that has been run against a SimCT dataset with the simulated genomic and transcriptional variations it contains to give an accurate performance evaluation in addressing specific biological question. We used these tools to simulate a real-world genomic medicine question s involving the comparison of healthy and cancerous cells. Results revealed that performance in addressing a particular biological context varied significantly depending on the choice of tools and settings used. We also found that by combining the output of certain pipelines, substantial performance improvements could be achieved. CONCLUSION: Our research emphasizes the importance of selecting and configuring bioinformatic tools for the specific biological question being investigated to obtain optimal results. Pipeline designers, developers and users should include benchmarking in the context of their biological question as part of their design and quality control process. Our SimBA suite of benchmarking tools provides a reliable basis for comparing the performance of RNA-Seq bioinformatics pipelines in addressing a specific biological question. We would like to see the creation of a reference corpus of data-sets that would allow accurate comparison between benchmarks performed by different groups and the publication of more benchmarks based on this public corpus. SimBA software and data-set are available at http://cractools.gforge.inria.fr/softwares/simba/ .

MOG antibody-related disorders: common features and uncommon presentations.

Myelin oligodendrocyte glycoprotein antibodies (MOG-Ab) have been reported in acute demyelinating encephalomyelitis (ADEM), optic neuritis (ON), and neuromyelitis optica spectrum disorders (NMOSD) in adults and pediatrics. We aimed to delineate the common features of MOG-Ab-related disorders in children and adults, and report uncommon presentations. Twenty-seven consecutive pediatric and adult patients testing positive for MOG-Ab, with a minimum follow-up of 6 months, were included. Comprehensive epidemiological, clinical, radiological, and laboratory data were retrospectively analyzed. Additionally, we compared radiological features between ADEM MOG-Ab-positive patients, and a group of ADEM MOG-Ab-negative ones, recruited during the same period. Among the whole cohort, 13 (48.1%) were pediatric, and 14 (51.9%) were female. MOG-Ab-related disorders comprised eight ADEM, eight ON, five isolated myelitis, four with NMOSD and two patients with multiple sclerosis, at last follow-up. After a median follow-up of 17.8 months, 11 (40.7%) patients presented a relapse. The most frequent clinical phenotype at onset was encephalopathy in pediatrics (53.9%) and myelitis in adults (50%) (p = 0.013). There were no other differences between both groups. When comparing ADEM MOG-Ab positive and negative patients, bilateral thalamic lesions were more often found in the positive group (p = 0.010). Unusual presentations were identified in three patients: patchy spinal cord gadolinium-enhancing lesions, an associated teratoma, and one presented with status epilepticus. MOG-Ab-related disorders shared common clinical and prognostic features, but encompass a spectrum wider than recently reported.

New chimeric RNAs in acute myeloid leukemia.

Background: High-throughput next generation sequencing (NGS) technologies enable the detection of biomarkers used for tumor classification, disease monitoring and cancer therapy. Whole-transcriptome analysis using RNA-seq is important, not only as a means of understanding the mechanisms responsible for complex diseases but also to efficiently identify novel genes/exons, splice isoforms, RNA editing, allele-specific mutations, differential gene expression and fusion-transcripts or chimeric RNA (chRNA). Methods: We used Crac, a tool that uses genomic locations and local coverage to classify biological events and directly infer splice and chimeric junctions within a single read. Crac's algorithm extracts transcriptional chimeric events irrespective of annotation with a high sensitivity, and CracTools was used to aggregate, annotate and filter the chRNA reads. The selected chRNA candidates were validated by real time PCR and sequencing.  In order to check the tumor specific expression of chRNA, we analyzed a publicly available dataset using a new tag search approach. Results:  We present data related to acute myeloid leukemia (AML) RNA-seq analysis. We highlight novel biological cases of chRNA, in addition to previously well characterized leukemia chRNA. We have identified and validated 17 chRNAs among 3 AML patients: 10 from an AML patient with a translocation between chromosomes 15 and 17 (AML-t(15;17), 4  from patient with normal karyotype (AML-NK) 3 from a patient with chromosomal 16 inversion (AML-inv16). The new fusion transcripts can be classified into four groups according to the exon organization. Conclusions:  All groups suggest complex but distinct synthesis mechanisms involving either collinear exons of different genes, non-collinear exons, or exons of different chromosomes. Finally, we check tumor-specific expression in a larger RNA-seq AML cohort and identify new AML biomarkers that could improve diagnosis and prognosis of AML.

On the evaluation of the fidelity of supervised classifiers in the prediction of chimeric RNAs.

BACKGROUND: High-throughput sequencing technology and bioinformatics have identified chimeric RNAs (chRNAs), raising the possibility of chRNAs expressing particularly in diseases can be used as potential biomarkers in both diagnosis and prognosis. RESULTS: The task of discriminating true chRNAs from the false ones poses an interesting Machine Learning (ML) challenge. First of all, the sequencing data may contain false reads due to technical artifacts and during the analysis process, bioinformatics tools may generate false positives due to methodological biases. Moreover, if we succeed to have a proper set of observations (enough sequencing data) about true chRNAs, chances are that the devised model can not be able to generalize beyond it. Like any other machine learning problem, the first big issue is finding the good data to build models. As far as we were concerned, there is no common benchmark data available for chRNAs detection. The definition of a classification baseline is lacking in the related literature too. In this work we are moving towards benchmark data and an evaluation of the fidelity of supervised classifiers in the prediction of chRNAs. CONCLUSIONS: We proposed a modelization strategy that can be used to increase the tools performances in context of chRNA classification based on a simulated data generator, that permit to continuously integrate new complex chimeric events. The pipeline incorporated a genome mutation process and simulated RNA-seq data. The reads within distinct depth were aligned and analysed by CRAC that integrates genomic location and local coverage, allowing biological predictions at the read scale. Additionally, these reads were functionally annotated and aggregated to form chRNAs events, making it possible to evaluate ML methods (classifiers) performance in both levels of reads and events. Ensemble learning strategies demonstrated to be more robust to this classification problem, providing an average AUC performance of 95 % (ACC=94 %, Kappa=0.87 %). The resulting classification models were also tested on real RNA-seq data from a set of twenty-seven patients with acute myeloid leukemia (AML).

Long non-coding RNAs in human early embryonic development and their potential in ART.

BACKGROUND: Human long non-coding RNAs (lncRNAs) are an emerging category of transcripts with increasingly documented functional roles during development. LncRNAs and roles during human early embryo development have recently begun to be unravelled. OBJECTIVE AND RATIONALE: This review summarizes the most recent knowledge on lncRNAs and focuses on their expression patterns and role during early human embryo development and in pluripotent stem cells (PSCs). Public mRNA sequencing (mRNA-seq) data were used to illustrate these expression signatures. SEARCH METHODS: The PubMed and EMBASE databases were first interrogated using specific terms, such as 'lncRNAs', to get an extensive overview on lncRNAs up to February 2016, and then using 'human lncRNAs' and 'embryo', 'development', or 'PSCs' to focus on lncRNAs involved in human embryo development or in PSC.Recently published RNA-seq data from human oocytes and pre-implantation embryos (including single-cell data), PSC and a panel of normal and malignant adult tissues were used to describe the specific expression patterns of some lncRNAs in early human embryos. OUTCOMES: The existence and the crucial role of lncRNAs in many important biological phenomena in each branch of the life tree are now well documented. The number of identified lncRNAs is rapidly increasing and has already outnumbered that of protein-coding genes. Unlike small non-coding RNAs, a variety of mechanisms of action have been proposed for lncRNAs. The functional role of lncRNAs has been demonstrated in many biological and developmental processes, including cell pluripotency induction, X-inactivation or gene imprinting. Analysis of RNA-seq data highlights that lncRNA abundance changes significantly during human early embryonic development. This suggests that lncRNAs could represent candidate biomarkers for developing non-invasive tests for oocyte or embryo quality. Finally, some of these lncRNAs are also expressed in human cancer tissues, suggesting that reactivation of an embryonic lncRNA program may contribute to human malignancies. WIDER IMPLICATIONS: LncRNAs are emerging potential key players in gene expression regulation. Analysis of RNA-seq data from human pre-implantation embryos identified lncRNA signatures that are specific to this critical step. We anticipate that further studies will show that these new transcripts are major regulators of embryo development. These findings might also be used to develop new tests/treatments for improving the pregnancy success rate in IVF procedures or for regenerative medicine applications involving PSC.

Combining DGE and RNA-sequencing data to identify new polyA+ non-coding transcripts in the human genome.

Recent sequencing technologies that allow massive parallel production of short reads are the method of choice for transcriptome analysis. Particularly, digital gene expression (DGE) technologies produce a large dynamic range of expression data by generating short tag signatures for each cell transcript. These tags can be mapped back to a reference genome to identify new transcribed regions that can be further covered by RNA-sequencing (RNA-Seq) reads. Here, we applied an integrated bioinformatics approach that combines DGE tags, RNA-Seq, tiling array expression data and species-comparison to explore new transcriptional regions and their specific biological features, particularly tissue expression or conservation. We analysed tags from a large DGE data set (designated as 'TranscriRef'). We then annotated 750,000 tags that were uniquely mapped to the human genome according to Ensembl. We retained transcripts originating from both DNA strands and categorized tags corresponding to protein-coding genes, antisense, intronic- or intergenic-transcribed regions and computed their overlap with annotated non-coding transcripts. Using this bioinformatics approach, we identified ∼34,000 novel transcribed regions located outside the boundaries of known protein-coding genes. As demonstrated using sequencing data from human pluripotent stem cells for biological validation, the method could be easily applied for the selection of tissue-specific candidate transcripts. DigitagCT is available at http://cractools.gforge.inria.fr/softwares/digitagct.

CRAC: an integrated approach to the analysis of RNA-seq reads.

A large number of RNA-sequencing studies set out to predict mutations, splice junctions or fusion RNAs. We propose a method, CRAC, that integrates genomic locations and local coverage to enable such predictions to be made directly from RNA-seq read analysis. A k-mer profiling approach detects candidate mutations, indels and splice or chimeric junctions in each single read. CRAC increases precision compared with existing tools, reaching 99:5% for splice junctions, without losing sensitivity. Importantly, CRAC predictions improve with read length. In cancer libraries, CRAC recovered 74% of validated fusion RNAs and predicted novel recurrent chimeric junctions. CRAC is available at http://crac.gforge.inria.fr.

Querying large read collections in main memory: a versatile data structure.

BACKGROUND: High Throughput Sequencing (HTS) is now heavily exploited for genome (re-) sequencing, metagenomics, epigenomics, and transcriptomics and requires different, but computer intensive bioinformatic analyses. When a reference genome is available, mapping reads on it is the first step of this analysis. Read mapping programs owe their efficiency to the use of involved genome indexing data structures, like the Burrows-Wheeler transform. Recent solutions index both the genome, and the k-mers of the reads using hash-tables to further increase efficiency and accuracy. In various contexts (e.g. assembly or transcriptome analysis), read processing requires to determine the sub-collection of reads that are related to a given sequence, which is done by searching for some k-mers in the reads. Currently, many developments have focused on genome indexing structures for read mapping, but the question of read indexing remains broadly unexplored. However, the increase in sequence throughput urges for new algorithmic solutions to query large read collections efficiently. RESULTS: Here, we present a solution, named Gk arrays, to index large collections of reads, an algorithm to build the structure, and procedures to query it. Once constructed, the index structure is kept in main memory and is repeatedly accessed to answer queries like "given a k-mer, get the reads containing this k-mer (once/at least once)". We compared our structure to other solutions that adapt uncompressed indexing structures designed for long texts and show that it processes queries fast, while requiring much less memory. Our structure can thus handle larger read collections. We provide examples where such queries are adapted to different types of read analysis (SNP detection, assembly, RNA-Seq). CONCLUSIONS: Gk arrays constitute a versatile data structure that enables fast and more accurate read analysis in various contexts. The Gk arrays provide a flexible brick to design innovative programs that mine efficiently genomics, epigenomics, metagenomics, or transcriptomics reads. The Gk arrays library is available under Cecill (GPL compliant) license from http://www.atgc-montpellier.fr/ngs/.

Using reads to annotate the genome: influence of length, background distribution, and sequence errors on prediction capacity.

Ultra high-throughput sequencing is used to analyse the transcriptome or interactome at unprecedented depth on a genome-wide scale. These techniques yield short sequence reads that are then mapped on a genome sequence to predict putatively transcribed or protein-interacting regions. We argue that factors such as background distribution, sequence errors, and read length impact on the prediction capacity of sequence census experiments. Here we suggest a computational approach to measure these factors and analyse their influence on both transcriptomic and epigenomic assays. This investigation provides new clues on both methodological and biological issues. For instance, by analysing chromatin immunoprecipitation read sets, we estimate that 4.6% of reads are affected by SNPs. We show that, although the nucleotide error probability is low, it significantly increases with the position in the sequence. Choosing a read length above 19 bp practically eliminates the risk of finding irrelevant positions, while above 20 bp the number of uniquely mapped reads decreases. With our procedure, we obtain 0.6% false positives among genomic locations. Hence, even rare signatures should identify biologically relevant regions, if they are mapped on the genome. This indicates that digital transcriptomics may help to characterize the wealth of yet undiscovered, low-abundance transcripts.

Solid-phase synthesis of dysidiolide-derived protein phosphatase inhibitors.

Biologically active natural products can be regarded as evolutionary selected and biologically validated starting points in structural space for the development of compound libraries. For libraries designed and synthesized around a given natural product, a higher hit rate and the identification of biologically relevant hits can be expected, justifying a probably higher investment in the development of the corresponding syntheses. This approach requires the development of complex multistep reaction sequences on the solid phase. Employing the protein phosphatase Cdc25 inhibitor dysidiolide as an example, we demonstrate that this goal can be achieved successfully. The reaction sequences developed led to dysidiolide analogues in overall 8-12 linear steps with the longest sequence on the solid support amounting to up to 11 sequential transformations. The desired products were obtained in overall yields ranging from 6% to 27% and in multimilligram amounts starting from 100 mg of resin. The transformations applied include a variety of very different reaction types widely used in organic synthesis (i.e., an asymmetric cycloaddition employing a removable chiral auxiliary, different organometallic transformations, olefination reactions, different oxidation reactions, acidic hydrolyses, and a nucleophilic substitution). Biological investigation of the eight dysidiolide analogues synthesized showed that they inhibit Cdc25C in the low micromolar range with the IC(50) value varying by a factor of 20 and that they display considerable and differing biological activities in cytotoxicity assays employing different cancer cell lines.