14977 results for "cancer studies using rna-seq"

in 24.59 milliseconds.

• Detection of ctDNA in Plasma of Patients with Clinically Localised Prostate Cancer is Associated with Rapid Disease Progression

  Purpose: DNA originating from degenerate tumour cells can be detected in the circulation in many tumour types, where it can be used as a marker of disease burden as well as to monitor treatment response. Although circulating tumour DNA (ctDNA) measurement has prognostic/predictive value in metastatic prostate cancer, its utility in localised disease is unknown. Patients and Methods: We performed whole genome sequencing of tumour-normal pairs in eight patients with clinically localised disease undergoing prostatectomy, identifying high confidence genomic aberrations. A bespoke DNA capture and amplification panel against the highest prevalence, highest confidence aberrations for each individual was designed and used to interrogate ctDNA isolated from plasma prospectively obtained pre- and post- (24 hours and 6 weeks) surgery. In a separate cohort (n=189) we identified the presence of ctDNA TP53 mutations in pre-operative plasma in a retrospective cohort, and determined its association with biochemical- and metastasis-free survival. Results: Tumour variants in ctDNA were positively identified pre-treatment in two of eight patients, which in both cases remained detectable postoperatively. Patients with tumour variants in ctDNA had extremely rapid disease recurrence and progression compared to those where variants could not be detected. In terms of aberrations targeted, single nucleotide and structural variants outperformed indels and copy number aberrations. Detection of ctDNA TP53 mutations was associated with a significantly shorter metastasis-free survival (6.2 vs. 9.5 years (HR 2.4; 95% CIs 1.2-4.8, p = 0.014)). Conclusions: CtDNA is uncommonly detected in localised prostate cancer but its presence portends more rapidly progressive disease.

  Study EGAS00001004636

• A higher ctDNA fraction decreases survival in regorafenib-treated metastatic colorectal cancer patients

  The predictive effect of circulating tumor DNA (ctDNA) in colorectal cancer (CRC) treatment is still highly discussed. The primary objective of this study was to investigate a possible prognostic/predictive value of ctDNA under regorafenib treatment. This prospective multicenter translational biomarker phase II pilot study enrolled 30 metastatic CRC patients (67% men, 33% women) treated with regorafenib. ctDNA was assessed in plasma before treatment start and at defined time points during administration. Measurement of tumor fraction as well as mutation and copy number analysis of CRC driver genes were performed by next generation sequencing approaches. Multivariate analyses for survival and treatment efficacy were adjusted to age, gender and ECOG. Disease control rate was 30%. Median tumor fraction at baseline was 18.5% (0-49.9). Mutations in CRC driver genes or genes involved in angiogenesis were identified in 25 patients (83.3%). KRAS mutations were detected in 13 out of 14 KRAS positive tumors, in three patients without KRAS mutation in the respective tumor acquired mutations as a consequence of prior anti-EGFR treatment were detected. In a subset of patients, novel occurring mutations or focal amplifications were detected. A tumor fraction of 5% and higher at baseline was significantly associated with a decreased OS (p=0.022; HHR 3.110 (95%CI: 1.2-8.2). ctDNA is detectable in a high proportion of mCRC patients. Higher ctDNA levels are associated with survival among regorafenib treatment. Moreover, our data highlight the benefit of a combined evaluation of mutations and somatic copy number alterations (SCNA) in advanced cancer patients.

  Study EGAS00001004491

• Colorectal cancer study

  Matrix metalloproteinase-11 (MMP11) is an enzyme with proteolytic activity against matrix and non-matrix proteins. Although most MMPs are secreted as inactive proenzymes and they are later activated extracellularly, MMP11 is activated intracellularly by furin within the constitutive secretory pathway. It is a key factor in physiological tissue remodeling and its alteration may play an important role in the progression of epithelial malignancies and other diseases. TCGA colon and colorectal adenocarcinoma data showed that upregulation of MMP11 expression correlates with tumorigenesis and malignancy. Here, we provide evidence that a germline variant in the MMP11 gene (NM_005940: c.232C>T; p.(Pro78Ser)), identified by whole exome sequencing, can increase the tumorigenic properties of colorectal cancer (CRC) cells. P78S is located in the prodomain region, which is responsible for blocking MMP11’s protease activity. This variant was detected in the proband and all the cancer-affected family members analyzed, while it was not detected in healthy relatives. In silico analyses predict that P78S could have an impact on the activation of the enzyme. Furthermore, our in vitro analyses show that the expression of P78S in HCT116 cells increases tumor cell invasion and proliferation. In summary, our results show that this variant could modify the structure of the MMP11 prodomain, producing a premature or uncontrolled activation of the enzyme that may contribute to an early CRC onset in these patients. The study of this gene in other CRC cases will provide further information about its role in CRC development, which might improve patient treatment in the future.

  Study EGAS00001006489

• PD-L1 blockade in combination with carboplatin as immune induction in metastatic lobular breast cancer: the GELATO-trial

  Invasive lobular breast cancer (ILC) is the second most common histological breast cancer subtype but published data on trials specific for ILC are so far lacking. Translational research revealed that a subset of ILCs may be immune-related and more sensitive to DNA-damaging agents such as platinum. In murine ILC models, synergy between immune checkpoint blockade and platinum has been observed. Here, we tested this concept in the phase II, GELATO-trial (NCT03147040), in which patients with metastatic ILC were treated with weekly carboplatin (AUC 1.5) as immune induction treatment for 12 weeks and atezolizumab (PD-L1 blockade; every three weeks) from the third week onwards until disease progression. Four out of 23 evaluable patients had a partial response (17%, 95%CI 5-39%) and two patients had stable disease for at least 24 weeks, resulting in a clinical benefit rate of 26% (95%CI 10-48%). Out of these six patients, four patients had triple-negative ILC (TN-ILC). In serial biopsies of metastatic lesions, we observed higher CD8 T-cell infiltration, expression of immune checkpoints, and exhausted T cells upon carboplatin/PD-L1 blockade. This is the first report of a clinical trial specifically for ILC and we demonstrate promising anti-tumor activity of atezolizumab with carboplatin as immune induction, in particular for TN-ILC. While activity of carboplatin/PD-L1 blockade in classical ER+ ILC was limited, our translational data yield important insights for the design of highly needed clinical trials in ILC.

  Study EGAS00001006902

• Genomic landscape of oral cancers (Illumina RNA-Seq)

  Human papillomavirus (HPV) is a necessary but insufficient cause of a subset of oral squamous cell carcinomas (OSCC) that is increasing markedly in frequency. To identify contributory, secondary genetic alterations in these cancers, we used comprehensive genomics methods to compare 149 HPV-positive and 335 HPV-negative OSCC tumor/normal pairs. Different behavioral risk factors underlying the two OSCC types were reflected in distinctive genomic mutational signatures. In HPV-positive OSCC, the signatures of APOBEC cytosine deaminase editing, associated with anti-viral immunity, were strongly linked to overall mutational burden. By contrast, in HPV-negative OSCC, T>C substitutions in the sequence context 5’-ATN-3’ correlated with tobacco exposure. Universal expression of HPV E6*1 and E7 oncogenes was a sine qua non of HPV-positive OSCC. Significant enrichment of somatic mutations was confirmed or newly identified in PIK3CA, KMT2D, FGFR3, FBXW7, DDX3X, PTEN, TRAF3, RB1, CYLD, RIPK4, ZNF750, EP300, CASZ1, TAF5, RBL1, IFNGR1 and NFKBIA. Of these, many affect host pathways already targeted by HPV oncoproteins, including the p53 and pRB pathways, or disrupt host defenses against viral infections, including interferon and nuclear factor-B signaling. Frequent copy number changes were associated with concordant changes in gene expression. Chr. 11q (including CCND1) and 14q (including DICER1 and AKT1) were recurrently lost in HPV-positive OSCC, in contrast to their gains in HPV-negative OSCC. High-ranking variant allele fractions implicated ZNF750, PIK3CA and EP300 mutations as candidate driver events in HPV-positive cancers. We conclude that virus-host interactions cooperatively shape the unique genetic features of these cancers, distinguishing them from their HPV-negative counterparts.

  Study EGAS00001003237

• Mutational dynamics of triple negative breast cancer over neoadjuvant chemotherapy treatment reveal frequent whole genome duplication events

  Background Triple-negative breast cancer (TNBC) is associated with poor survival rate and high genomic instability, generating complex tumour genomes. However, the processes generating this complexity are poorly studied over time. Here, we study the temporal dynamics of TNBC somatic mutations, revealing major transitions in tumour genome evolution, from diagnostic biopsies, through treatment, to cancer remission or recurrence. Methods Deep whole exome sequencing and CUTseq, a reduced representation whole genome sequencing approach, were performed in parallel, to comprehensively identify short nucleotide variants (SNVs), copy number alterations (CNAs) and aneuploidies genome-wide. We profiled (N=74) tumour samples from 22 patients across the course of disease, including spatially diverse samples from many tumours, allowing us to observe the gain and loss of candidate driver variants over time. Results As expected, tumour infiltrating lymphocyte (TIL) levels and residual cancer burden (RCB) score were negatively correlated, with good responses to neoadjuvant chemotherapy (NACT) seen in pre-treatment samples with high infiltration. In general, SNV mutational burden remained stable across disease progression and RCB classes. Recurrent SNVs across the cohort implicated several known TNBC driver genes in disease progression and response to treatment, with TP53, MICA, CYP2D6, BRCA1 and BRCA2 frequently altered. However, the presence of candidate driver SNVs in these genes often varied throughout a patient’s treatment, including the loss of those seen in a given driver gene in pre-treatment samples, but gain of novel variants in the same gene at later time points. Dramatic structural dynamics were seen in all tumours, including abundant CNA including chromosome arm aneuploidies seen in pre-treatment samples, followed by frequent loss of these alterations post-treatment, and their re-emergence at recurrence. Whole genome duplication (WGD) events appear to drive these dynamics, with a higher frequency of pre-treatment WGD seen in patients with the best response to NACT. Conclusions Comprehensive longitudinal profiling demonstrates the complex interplay of SNVs and copy number alterations during TNBC progression, leading to diverse evolutionary trajectories and patient outcomes. Complex mutational patterns emerge as important features during progression, with WGD events emerging as potential candidate biomarkers of response at both tumour establishment and recurrence.

  Study EGAS00001008261

• Disorders/Differences of Sex Development (DSD) Study Performed at UCLA in Collaboration with the DSD-Translational Research Network (DSD-TRN), with the Support of the Gabriella Miller Kids First Pediatric Research Program

  The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). Both childhood cancers and structural birth defects are critical and costly conditions associated with substantial morbidity and mortality. Elucidating the underlying genetic etiology of these diseases has the potential to profoundly improve preventative measures, diagnostics, and therapeutic interventions. Whole Genome Sequence (WGS) and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Disorders/Differences of Sex Development (DSD) are congenital conditions in which development of chromosomal, gonadal, or anatomic sex is atypical. DSD are chronic medical conditions collectively affecting ~1% of the population, frequently requiring life-long care by multiple specialists, and carrying a significant public health burden. Some are associated with life-threatening events, such as adrenal crises in Congenital Adrenal Hyperplasia (CAH). DSD are also associated with increased infertility, cancer, gender dysphoria risks, psychosocial distress and pervasive challenges to health-related quality of life (HRQoL) for patients and families. DSD are broadly classified into three categories: sex chromosome DSD, 46,XY DSD and 46,XX DSD, and further classified according to the type of gonad found in the patient (ovary, testis, ovotestis). We were able to increase significantly the diagnostic success for DSD using Whole Exome Sequencing (WES), with the identification of disease-causing and likely pathogenic variants in a third of a cohort of 46,XY patients. We have therefore proposed a shift in the diagnostic approach to DSD to use next-gen sequencing as a first-line clinical test, which could lead to faster and more accurate diagnosis, and orient further clinical management, limiting unnecessary, costly, and often invasive endocrine testing and imaging. However many remain unexplained (over half of the XY cases, a significant minority of XX cases, including most ovotesticular DSD, and most syndromic cases). In addition, the very large phenotypic variability in cases with known variants in the same gene is unexplained. We here propose to use Whole-Genome Sequencing (WGS), which dramatically improves upon exome sequencing, covering both coding and non-coding parts of the genome more uniformly, as an approach to not only improve diagnostic yield, but also to identify novel genes and regulatory elements involved in DSD.

  Study phs001178

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? TThe most suitable archive for your data is dependent on the type of data you are wishing to submit and whether the data require public or controlled access. Public access is defined as complete and open access to all submitted data. On the contrary, controlled access, exerted by the EGA, requires formal applications to be made to access the submitted data files and metadata. EGA only accepts human-derived data subject to controlled access. If your submission contains other types of data, please choose the appropriate repository for it (see image below): ENA, EVA, ArrayExpress, BioSD and GWAS catalog.  Should your submission be subjected to controlled access? Data access conditions are normally defined in the original informed consent agreements signed by the participants involved in your study. All data submitted to the EGA is subject to controlled access. These consents prevent the derived data files, potentially identifiable, from being dispersed by open and public access. Controlled access data often refers to human data derived from medical research and consortium projects. If in doubt, consult the informed consent agreements that apply to your study The EGA enables you to hold a submission before publication. What data types can be submitted to the EGA? Data types accepted by the EGA can be split into three categories: Sequences: both in generic and platform-specific formats. Array-based: from raw signal files to processed matrices. Phenotypes: all possible phenotype formats are accepted. All manufacturer-specific raw data formats derived from major next generation sequencing platforms are accepted. Also generic sequence formats: flat reads in a FASTQ file, aligned sequences (BAM or CRAM files) as well as sequence variation files in VCF format. All array-based technologies are accepted, including raw data, intensity and analysis files, without any restriction on data formats accepted. We also accept and distribute phenotype data (associated to the samples) in almost any format: from an image to a README file. How long does a submission take? Submissions to EGA come in a variety of formats and sizes, thus it is difficult for us to exactly predict how long a submission will take. We, therefore, advise all of our submitters to allow as much time as possible to make a submission. Based on previous records, we anticipate that the submission process may take at least one month. The submitter’s familiarity with the procedures, possible technical issues that may arise during submission and the amount of data that is being submitted are the main factors that will affect the length of the submission process. However, each step of a regular submission should be considered when estimating the time it would take: Encryption of the files Upload of the files Metadata submission Archival of the files Release of the study and datasets to EGA webpage For example, the upload of the files depends on the submission size, while metadata submission mainly relies on each submitter’s expertise. Further, some steps (e.g. answering to inquiries) depend on the EGA Helpdesk team, which may take some days to be processed during busy times Is data deposited in the EGA secure? The EGA set-up consists of a secure computing facility for data processing, a shared EBI set-up for data submissions and distribution of data via data requests made through the EGA website. Data is also copied in the Barcelona Supercomputing Center (BSC) infrastructure, where all stored and distributed data is encrypted Data is encrypted along the submission process and stored securely, granting its access to authorised users exclusively. During the download process, through our Python Client or Aspera, all requested data is downloaded over secure https connections. All data at the EGA is encrypted, and only accessible (for log-in and download) through secure protocols. For further information please, visit our security overview. What documentation do I need to provide? All submissions require policy’s documentation: 'Data Access Agreement (DAA)', 'Data Processing Agreement (DPA)' and 'Authorized Submitters Formulary'. The data processors (EGA) and the data owners will also sign the DPA. Will all metadata be public? Among the submitted metadata we need to make the distinction between identifiable and unidentifiable metadata: (1) the former may allow the identification of the human the sample derived from (e.g. detailed geographical providence, personal name, family ancestry…); (2) while the latter can be used to interpret the data without compromising the anonymity of the patients. The majority of the metadata submitted to the EGA corresponds to the unidentifiable category (e.g. sequencer's model). This type of metadata is publicly available on the EGA website and other EBI resources/partners’ websites. On the other hand, some parts samples’ metadata are subject to being identifiable, and thus only accessible by authorized data requesters, with the exception of: 5 submitter-defined attributes of the sample: alias, title, subject_id, gender and phenotype. It is the submitter’s responsibility not to submit sensitive metadata in these public fields. 3 anonymised fields that pinpoint the sample record in archivals: sample’s EGA stable ID (EGAN…), BioSample ID (SAMEA…) and submitter’s center name. During Submission Are there any sample specific requirements for EGA? All samples submitted to the EGA must include the attributes of biological sex, subject ID (anonymised individual identifier) and phenotype information. These are critical for data findability and its analysis, and we highly recommend using controlled ontology terms where applicable. For example: defining tumour and non-tumour samples and/or defining disease state. The EGA recommends using the Experimental Factor Ontology Database to find ontologized terms that describe your sample phenotypes. How do I get an accession number to use in my publication? You will receive your study accession number (EGAS…) upon complete your submission, either: Programmatically. As soon as the metadata is submitted and validated your study will be assigned an accession number that will be given in the submission’s response. Manually registering your study and relevant metadata using the online metadata submission tool: the EGA submitter Portal. How are files uploaded to the EGA? Data files are uploaded into private submission drop boxes (i.e. environments to which you are granted access and where you can transfer your files) using INBOX or FTP. These spaces are provided as part of the submission procedure. Before uploading any file, you must encrypt your files, . Only encrypted files shall be uploaded to the drop boxes. Why does data need to to encrypted for my submitted files? It is one of the security steps the EGA has implemented. In case of a security breach, people without the proper encryption key will not be able to read or use the information that could have been leaked. This measure is essential when working with sensitive data, such as controlled access human data. All submitters must use crypt4gh to create EGA compliant files prior to uploading them. This encryption is GPG-based, using EGA’s public key. Why are my files not available if I see them in the INBOX? There exists a time window between the data upload and the availability of such files via the Submitter Portal. For this reason, some metadata (run and analysis objects) cannot be registered until at least 24 hours after the files have been uploaded to your box. Why are MD5 sum values generated for my submitted files? We require pre- and post- encryption MD5 (message-digest) checksum values to be provided for all submitted files. These 128-bit values are computed using the content of each file, creating unique sequences that allow us to ensure that file integrity has been maintained during the transfer process. In other words, if the MD5 checksums we generate and those you generated match, we infer that the content of the transferred files is correct (i.e. files are not corrupted or truncated). MD5 checksums are computed automatically using the crypt4gh tool provided. Your submission will not be accepted and may be significantly delayed if you do not provide MD5 checksum values for all data files in the required format. How can I check if my files are correctly uploaded to the inbox? It is important to check the status pf your file so you know whether your files are in the inbox, being processed, or if there is any issue with one of them. In order to check this, you should: Look for the file locally. Drag and drop it to the file table. Then bars will appear on the table, which means that we are processing it. Green: The files checksum are correct and your file will move to “ingested files”. No further actions are needed from your end.. Red: The files checksum does not match and your file needs to be re-uploaded. Please re-upload relevant files to your inbox using the same path. After Submission How do I use my accession number in my publication? We suggest the use of the below template, using your study accession ID (EGAS…) : Data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found at https://ega-archive.org and "The European Genome-phenome Archive of human data consented for biomedical research" Your study ID will be the one that groups your whole submission, and thus its usage is recommended as such. Nevertheless, all metadata submitted to EGA hold a unique and persistent identifier (starting with EGA…) that can be used to identify specific records. For example, you could reference a specific dataset (EGAD…) or sample (EGAN…) in your publications (see full list of identifiers). How do I make my data searchable? Once you have finalized your submission, you can schedule the data release. Please take into account that the release process needs time for the files to be archived in our system, and for the Helpdesk team to validate your submission. Can I withdraw (meta)data from the EGA? We have methods in place for the secure removal of deposited (meta)data. Contact EGA-helpdesk for further details. EGA complies with FAIRness of (meta)data, and thus, even when the data is removed we keep an entry for their identifiers in our system. In other words, we execute a soft delete on canceled objects (e.g. a study): metadata is still stored in our systems, but it loses all links, cannot be queried and data files cannot be retrieved anymore. The reason behind this behaviour is so that queries using withdrawn data properly respond back (see example of a canceled study). What happens to the data once it has been submitted to the EGA? When the data is submitted, the submitter can choose either keep their data private or schedule the release of their data.

  Documentation submission/metadata/submission/FAQ

• NIPT samples for systematic evaluation of BinDel, a software tool for detecting clinically significant microdeletions in low-coverage WGS-based NIPT samples

  This dataset contains three sets of samples. The first sample set contains euploid fetus pregnancies reported by NIPTIFY screening test and postnatal evaluation. Dataset was processed similarly to previously published guidelines from KU Leuven, with modifications [1]. Briefly, peripheral blood samples were collected in cell-free DNA BCT tubes (Streck, USA), and plasma was separated with standard dual centrifugation. Cell-free DNA was extracted from 3 ml plasma using MagMAX Cell-Free DNA Isolation Kit (ThermoFisher Scientific). Whole-genome libraries were prepared using the FOCUS (Fragmented DNA Compact Sequencing Assay, Competence Centre on Health Technologies, Estonia) NIPT method protocol with 12 cycles for the final PCR enrichment step. In the following quantification, equal amounts of 36 samples were pooled, and the quality and quantity of the pool were assessed on Agilent 2200 TapeStation (Agilent Technologies, USA). Whole genome sequencing was performed on the NextSeq 550 instrument (Illumina Inc.) with an average coverage of 0.32× (minimum 0.08 and maximum 0.42) and producing 85 bp single-end reads. The second sample set contains a single NIPT sample postnatally diagnosed with Prader-Willi syndrome. The sample was sequenced with Illumina NextSeq 500 platform, producing 85 bp single-end reads with an average per-sample coverage of 0.32× at the University of Tartu, Institute of Genomics Core Facility, according to the manufacturer’s standard protocols, as described previously [2]. The third sample set contains samples SC005 (SeraCare Life Sciences Inc lot #10446565), SC0042 (#10571706), and SC016 (#10560229). These are SeraCare Life Sciences Inc circulating cell-free DNA (ccfDNA) like mixture of human genomic DNA that consists of matched maternal and fetus. SC005 and SC0042 consist of matched DNA of maternal and fetus with DiGeorge Syndrome. SC016 is a custom-ordered DNA Mix with fetus DNA having a pathogenic loss of the terminal region of 20p13 and a pathogenic 3q29 duplication. SC016 was processed as the first sample set was processed, and SC0042 was processed as the second sample set was processed. Sample SC005 was processed once as was sample set 1 and once as was sample set 2 processed. This study was performed with the approval of the Research Ethics Committee of the University of Tartu (#352/M-12). 1. Bayindir B, Dehaspe L, Brison N, Brady P, Ardui S, Kammoun M, et al. Noninvasive prenatal testing using a novel analysis pipeline to screen for all autosomal fetal aneuploidies improves pregnancy management. Eur J Hum Genet. 2015;23: 1286– 1293. doi:10.1038/ejhg.2014.282 2. Žilina O, Rekker K, Kaplinski L, Sauk M, Paluoja P, Teder H, et al. Creating basis for introducing noninvasive prenatal testing in the Estonian public health setting. Prenat Diagn. 2019;39: 1262–1268. doi:10.1002/pd.5578

  Dataset EGAD00001009512

• How to encrypt files with EGACryptor

  File preparation Due to the processes used at the EGA for file archival the use of non-alphanumeric characters in a filename will cause issues in archival. By convention whitespaces in filenames are to be avoided and should be replaced with the underscore character (_). Before encrypting your files please make sure that any files that will be uploaded to EGA do not use special characters such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &  Crypt4gh EGACryptor Files encrypted with EGACryptor must be uploaded via FTP EGACryptor The EGACryptor v.2.0.0 is a JAVA-based application which enables submitters to produce EGA compliant encrypted files along with files for the encrypted and unencrypted md5sum for each file to be submitted. The application will generate an output folder that will by default mirror the directory structure containing the original files. This output folder can subsequently be uploaded to the EGA FTP staging area via an FTP or Aspera client. Download EgaCryptor Download EgaCryptor Using the EgaCryptor Using the EgaCryptor Encrypting single file Encrypting multiple files Encrypting all files in folder Points to note Troubleshooting Troubleshooting Download EGACryptor The required jar files can be obtained by downloading EgaCrytptor jar file After the file has been downloaded, extraction of the zipped archive is required. The EGACryptor has been built to work with Java Runtime Environments from version 6 and above and with the OpenJDK Environment. Please refer to the relevant resources for installation guidance. Installing the latest version of the OpenJDK will include the JCE files. If your installation of Java JRE is less than 1.8.0_151 will require the manual installation of the JCE Policy Files. You can verify the version of the Java SE Runtime Environment (JRE) installed by using the command: $ java -version If you need to install the JCE please follow the instructions below: Installing the JCE policy files (due to licensing terms and conditions the required policy files must be downloaded direct from the ORACLE website) : Download the unlimited strength JCE policy files (JRE 6 / JRE 7/ JRE 8) Uncompress and extract the downloaded file. This will create a subdirectory called JCE. This directory contains the following files: README.txt, COPYRIGHT.html, local_policy.jar and US_export_policy.jar Install the two policy JAR files by replacing the existing ones in your java home directory. Install the two policy JAR files by replacing the existing ones in your directory. The standard place for JCE jurisdiction policy JAR files is: /lib/security [Unix] or \lib\security [Win32] Notes: refers to the directory where the Java SE Runtime Environment (JRE) was installed. Additional performance enhancements that have been included in the EGACryptor V2.0.0: The ability to parallelise the processing of datasets through the use of the resources on a system. Multicore systems will allow the user to specify n-1 cores for an n-core system. The use of this feature on clusters may speed up the processing of datasets that have large file numbers but consult your local cluster guide to ensure that there are not monopolising resources that are needed by other system users. The default for this process remains single threaded. 3 levels of system usage can be specified. Full usage within the limits detailed above. A limited mode that will ensure that 50% of the system resources are available for other tasks. Maximum mode is limited to 75% of system resources, this allows encryption to be prioritised but allows for the system to be usable for light alternate tasks. Finally there is a throttling mode that allows you to specify the exact number of computational threads to be used. the EGACryptor is able to ingest a structured directory and will output a directory with the same structure containing the encrypted files along with the md5checksums for the plain and encrypted files. The entire output directory can then be uploaded to the EGA for archival. as with the input path, it is now possible to specify the output path. the options have been updated inline with the upgraded functionality. The tool can only be used via the command line. The EGACryptor is designed to perform a single task, encrypting your data, for upload of these files please refer to our uploading guide Using the EgaCryptor Below are the three ways on how the EGACryptor tool can be used: Encrypting a single file : java -jar ../EGA-Cryptor_2_0_0.jar -i example1.bam Encrypting multiple files : java -jar ../EGA-Cryptor_2_0_0.jar -i "example1.bam,example2.bam" Encrypting all the files within a folder java -jar ../EGA-Cryptor_2_0_0.jar -i path/to/target/directory By default the EGACryptor v2 will create a new output directory containing all encrypted files and the relevant checksums within the target directory. If a specific directory is desired this can be specified by using the -o flag. This can be achieved in a similar manner to the following example: java -jar ../EGA-Cryptor_2_0_0.jar -i /path/to/target/directory -o /path/to/output/directory The tool will output three files per input file: file.gpg ( encrypted file ) file.md5( file md5 sum value file ) file.gpg.md5 ( encrypted file md5 sum value file ) All output files must then be uploaded to your submission account using Aspera or FTP. Further documentation on how to upload files: FTP and Aspera. Points to note Remember to provide the path to EgaCryptor.jar and run the command from within the directory the file/s are located. ECryptor writes files to the source directory of your local file system, as a result you must have write-access permissions for the source directory. Troubleshooting If in doubt about the function of the EGACryptor it is recommended to first consult the built-in documentation. This can be accessessed by using the -h flag as stated in the following table. Built-in Commands Table: list of the command line options built into EGA-Cryptor v2.0.0. Command line Option Action --------------------- ----------- -f Set this option to allow application to create maximum threads to utilise full capacity of cores/processors available on machine -h Use this option to view the bult-in help menu * -i File(s) to encrypt. Provide file/folder path or comma separated file path if multiple files in double quotes -l Set this option to allow application to create maximum threads equals to 50% capacity of cores/processors available on machine -m Set this option to allow application to create maximum threads equals to 75% capacity of cores/processors available on machine -o Path of the output file. This is optional. If not provided then output files will be generated in the same path as that of source file (default: output- files) -t Set this option if user wants to control application to create maximum threads as specified. Application will calculate no. of cores/processors available on machine & will create threads accordingly Encryption Errors UnixFileSystem.createFileExclusively (Native Method) The error is thrown by UNIX ("UnixFileSystem.createFileExclusively(Native Method)"). It appears that the user does not have write-access to the file system where the file to be uploaded is located. EGACryptor always writes MD5 checksums into files before uploading them to the server, and these files are created in the same location where the uploaded file itself resides.Solution: address your directory permission issue and re-run the command. Install JCE Unlimited Strength Jurisdiction Policy files The JCE policy unlimited strength jurisdiction files should be installed according to your current java version If you are still facing difficulty with the EGACryptor v.2 after having consulted the documentation please contact the EGA Helpdesk.

  Documentation submission/data/file-preparation/egacryptor

• Netherlands Cancer Institute (NKI-AVL) general DAC

  Data is available under reasonable request. All data requests will be reviewed by the NKI IRB and must be supported by the Principal Investigator of the study. The researcher will need to sign a data access agreement with the NKI after approval. Go to https://ega.nki.nl to fill out the form to request access.

  Dac EGAC50000000055

• DELFI low-coverage WGS of plasma cfDNA

  The dataset "DELFI low-coverage WGS of plasma cfDNA" includes paired FASTQ files produced by WGS of 689 plasma cfDNA samples from 153 patients with colorectal cancer. WGS (100bp PE) was performed on a NovaSeq 6000 with a target depth of coverage of 8x.

  Dataset EGAD50000000630

• eSENSES control samples

  This dataset comprises BAM files from 18 control samples analyzed with the eSENSES targeted NGS panel. Spanning approximately 2 Mb, the panel features around 15,000 evenly distributed genome-wide SNPs, over 500 focal SNPs targeting key breast cancer driver regions, and more than 2,000 exons from 81 commonly altered genes.

  Dataset EGAD50000001167

• DNA sequencing of sgRNAs enriched from the CRISPR-Cas9 screened HCC organoids

  There are 12 samples. The project aims to find enriched or depleted sgRNAs to identify functional genes in HCC progression and lenvatinib resistance

  Study EGAS50000001130