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in 16.88 milliseconds.

• Whole transcriptome RNA sequencing as comprehensive diagnostic tool for acute myeloid leukemia.

  BackgroundAcute myeloid leukemia (AML) is characterized by uncontrolled proliferation of malignant hematopoietic cells in the bone marrow that are arrested in differentiation. In AML pathogenesis, hematopoietic progenitor cells acquire multiple genetic aberrations often occurring in the same set of genes that ultimately lead to malignant transformation. In the WHO classification 2016, six AML classes with different prognosis are identified by chromosomal translocations measured by standard cytogenetics. All balanced translocations produce fusion genes, except for t(3;3)/inv(3;3), which lead to overexpression of MECOM/EVI1 associated with poor prognosis. For accurate risk assessment of cytogenetically normal AML, four genes need to be screened to distinguish AML with mutated NPM1 in the absence of FLT3 mutations and AML with bi-allelic CEBPA mutations, which have a favorable prognosis, and AML with RUNX1 mutations which have a poor prognosis. Recently, a full genomic classification system has been proposed by Papaemmanuil et al. (NEJM 2016). In this system, the same six classes with chromosomal translocations are identified by standard cytogenetics as well as five additional classes characterized by genetic mutations in 14 different genes. AimThe aim of the study was to investigate whether whole transcriptome RNA-sequencing (RNAseq) can be used as single technology for classification of AML. MethodA panel of 100 AML were analyzed and a bio-informatics pipeline called HAMLET (Human AML Expedited Transcriptomics) was developed in which 4 modules are integrated to detect (1) fusion genes, (2) small variants in 13 recurrently mutated genes, (3) internal tandem duplications in FLT3 (FLT3-ITD) and partial tandem duplications in KMT2A (KMT2A-PTD) and (4) overexpression of MECOM/EVI1. All mutations that were called by HAMLET were validated by diagnostic data as available for all 100 AML or targeted PCR followed by Sanger or next generation sequencing on all positive AML and at least an equal number of negative cases. Results & discussionThe data showed that HAMLET accurately identified all genetic aberrations with high sensitivity and specificity. In addition, in 7 AML, fusion transcripts were detected that are not measured by standard cytogenetics including three cases that lack any class-defining lesion according to Papaemmanuil et al. Moreover, overexpression of MECOM/EVI1 was detected in two AML with inv(3) as well as in five cases without inv(3)/t(3;3), and a gene signature was measured to distinguish AML with CEBPA mutations with favorable prognosis. In conclusion, HAMLET provides a comprehensive and flexible pipeline for RNAseq analysis to retrieve all relevant information for current classification of AML as well as additional information that may improve classification in the future.

  Study EGAS00001003096

• DAC for "A Functional Network of Gastric-Cancer-Associated Splicing Events Controlled by Dysregulated Splicing Factors"

  Dac EGAC00001000675

• Genomic stratification and liquid biopsy in a rare adrenocortical carcinoma (ACC) case, with dual lung metastases

  Dac EGAC00001001138

• Oxidative phosphorylation is a key ontogenetic feature of monocyte immunometabolism promoting myeloid differentiation after birth

  Dac EGAC00001003420

• Human oligodendroglial progenitor cells (hGPCs/OPCs) derived from induced pluripotent stem cell (iPS) scCRISPRi/a-seq

  Dac EGAC50000000804

• Expression profiles and genetic makeup of metastases of a cancer of unknown primary.

  Study EGAS00001004059

• Concurrent germline and somatic pathogenic BAP1 variants in a patient with metastatic bladder cancer

  Study EGAS00001004055

• NPY methylated ctDNA is a promising biomarker for treatment response monitoring in metastatic colorectal cancer

  Study EGAS00001006820

• Transcriptome analyses of a large cohort of adult B cell acute lymphoblastic leukemia samples

  Study EGAS00001007217

• Characterization of a novel MEF2D-BCL9 fusion positive acute lymphoblastic leukemia cell line RNA-Seq derived

  Study EGAS00001006803

• 20200819_EGA_Qld_Melanoma.biomarkers

  A study looking at Germline and Somatic biomarkers using WES data only

  Dataset EGAD00001006374

• GAW16 Framingham and Simulated Data

  Important links to apply for individual-level data Genetic Analysis Workshop Instructions to Request Authorized Access Data Use Certification Requirements (DUC) Apply here for controlled access to individual level data Research Use Statement Questions regarding GAW16 should be directed to Vanessa Olmo at vanessa@business-endeavors.com. Problem 2: Description of the Framingham Heart Study In GAW16, we use data drawn from the Framingham Heart Study. The Framingham Heart Study — under the direction of National Heart, Lung, and Blood Institute; NHLBI — began in 1948 with the recruitment of adults from the town of Framingham, Massachusetts. At the time, little was known about the general causes of heart disease and stroke, but the death rates for cardiovascular disease (CVD) had been increasing steadily since the beginning of the 20th century and had become an American epidemic. The Framingham Heart Study is now conducted in collaboration with Boston University. The objective of the Framingham Heart Study was to identify the common factors or characteristics that contribute to CVD by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms of CVD or suffered a heart attack or stroke. Between 1948 and 1953 the researchers recruited 5,209 subjects (2,336 men and 2,873 women) between the ages of 29 and 62 from the town of Framingham, Massachusetts and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Subjects were recruited from lists of addresses recorded for the town. Two out of every three households were approached for participation in the study. While there was no intention to recruit families for family studies, the plan was to recruit all household members in the ages 30-60 within each house that was selected for study. Hence, many biologically related individuals were recruited, including 1644 spouse pairs. Since 1948, these participants have returned to the study every two years for a detailed medical history, physical examination, and laboratory tests. Now in 2008 at 60 years of follow up, there remain about 500 participants from this cohort. Between 1971 and 1975 the study enrolled a second-generation group — 5,124 of the original participants' children and the spouses of these children — to participate in similar examinations. 2,616 subjects are offspring of the original spouse pairs and 34 are stepchildren. A total of 898 offspring are children of cohort members where only one parent was a study participant and 1,576 are spouses of the offspring. The Offspring Cohort has been followed every four years through 2001 (except between Exams 1 and 2 with an intervening 8 years) using protocols similar to those used for study of the Original Cohort. Between 2002 and 2005 the study enrolled the third generation (Gen3) of the Framingham Heart Study - 4095 offspring of the second generation. None of their spouses were recruited. An additional 103 parents of this third generation, who were not recruited between 1971 and 1975, were also recruited at this time. The latter group is not included in the GAW16 data. With the recruitment of this third generation, the study has increasingly focused on genetic factors associated with the development of cardiovascular disease and its associated risk factors. To date, there is only one examination of this generation of participants. A description of the recruitment of this third generation and comparison with the earlier generations at their initial recruitment is presented in Splansky GL et al., 2007. Further information on the Study can be found at http://www.nhlbi.nih.gov/about/framingham/index.html. Genome-wide Dense SNP Scan in Framingham Heart Study Genetic studies did not begin in the FHS until the 1990s. In the late 1980s and through the 1990s DNA was extracted from blood samples of surviving FHS participants. In 2007, the FHS entered a new phase with the conduct of genotyping for the FHS SHARe (SNP Health Association Resource) project, for which dense SNP genotyping was performed using approximately 550,000 SNPs (GeneChip® Human Mapping 500K Array Set and the 50K Human Gene Focused Panel) in 10,775 samples (some duplicates) from the three generations of subjects (including over 900 pedigrees). Affymetrix conducted all genotyping for the FHS SHARe project, using the 250K Sty, 250K Nsp, and the supplemental 50K platforms. Eighty-nine percent of the DNA samples were collected during the 1990s. To maximize the power of the study, we also extracted DNA from 1133 blood samples, drawn from subjects who had no DNA, to include in the SHARe project. These samples had been sitting in our refrigerators for some time, a few as far back as the 1970s. We refer to these DNA samples as the legacy samples. These samples had a higher failure rate in the genotyping process (40%) than the other eighty-nine percent (3%). Affymetrix invoked its own criteria for a sample to succeed in genotyping. All non-legacy samples must succeed on all three platforms, while legacy samples needed to pass on at least one platform. When a sample failed, additional attempts were made. Samples that repeatedly failed 2-4 times were called failures. Other samples failed due to issues of genotyped sex identification not matching our records or low SNP concordance among SNPs common across arrays or contamination. Eighty-nine percent of the legacy samples for which genotyping results are available passed all three platforms. The genotyping data from the 10,043 samples from 9354 subjects that passed the Affymetrix criteria were additionally checked for gender consistency and consistency with family structure, resulting in genotyping data for 9,274 participants in FHS SHARe. Genotype calls were made with the BRLMM algorithm. The SHARe database is housed at the National Center for Biotechnology Information database of genotypes and phenotypes (NCBI dbGaP) and contains all ~550,000 SNPs. This genome-wide dense SNP scan and a subset of phenotypes from the Framingham Heart Study are the focus of the Genetic Analysis Workshop 16. Further information on the specific variables in the Problem 2 dataset can be found by clicking on the Documents tab at the top of the page. Problem 3: Description of the Simulated Data Set The focus of this simulation is gene discovery in genome-wide association scans (GWAS). The Framingham Heart Study data set (distributed as "Problem 2") is the basis for the FHS* simulated data. The pedigree structures are derived from the data distributed for Problem 2, and we distribute an accompanying triplet file (triplet_sim) containing person ID, father ID, mother ID, to ensure the identical subjects, pedigrees, and singletons are used in the simulated data analysis. Consistent with standard practice, founders and singletons are designated as subjects with both fshare and mshare equal to zero (missing). The simulated data includes a total of 6,479 subjects with both phenotype and genotype data, in 942 pedigrees distributed among 3 generations and 188 singletons. Data inclusion is consistent with the subjects' consent for use by both for-profit and not-for-profit researchers. The genotypes for all Problem 3 replicates are fixed as measured and distributed for Problem 2 for both the genomewide scan and the additional candidate gene SNPs, for a total of approximately 550,000 SNPs (GeneChip® Human Mapping 500K Array Set and the 50K Human Gene Focused Panel). Thus, to analyze the Problem 3 simulated data you also will need to download the Problem 2 genotypes. Note that there are slight discrepancies in counts between Problems 2 and 3 due to a change in consents between the two datasets. Several phenotypes that contribute to coronary heart disease (CHD) were simulated for all individuals with genotypes across three different time points, 10 years apart. All genotyped individuals have complete data; the effects of missing values can be investigated by user-specified missing value patterns. There are 200 longitudinal datasets created, based on the generating model, and each replication is found in a separate dataset. We suggest that if only one replication is to be analyzed, that it be replication 1 to enable more precise comparisons among analytical approaches. The 'shareid' will allow you to merge the simulated phenotype data with the Problem 2 genotype data, and reconstruction of the pedigrees using the distributed 'triplet_sim' file or for larger families' relationships with the triplet distributed with the Problem 2. The simulated data problem is further described in the associated readme file, and a data dictionary is provided defining all the variables. For disclosure of the generating model for these data, please contact Jean MacCluer at jean@sfbrgenetics.org.

  Study phs000128

• Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS)

  This pharmacogenomic study focused on the response of patients with MDD to an 8-week course of treatment with citalopram or escitalopram. Briefly, clinical assessments and research assessments were performed at each study visit as summarized in Table 1. Many patients continued on antidepressant medication after the 8 week study. Table 1: Study timeline and patient contacts. Week Consent/Med Hx Write Rx SI Qx SCID HRS-D17 QIDSC16 CGI-I 0 SC RC RC SC SC SC 4 RC RC SC SC RC 8 RC RC SC SC RC HRS-D17=Hamilton Rating Scale for Depression, QIDS-C16=Quick Inventory of Depressive Symptomatology, CGI-I=Clinical Global Impression-Improvement, SCID=Structured Clinical Interview for DSM-IV, SC=Study Coordinator, RC=Research Clinician. Enrollment and baseline assessment. Upon initial intake, the Study Coordinator (SC) obtained informed consent as well as clinical and demographic information, including history of bone marrow or liver transplant or blood transfusion within the previous 6 weeks, and prior history of treatment during the current major depressive episode. The SC completed the Structured Clinical Interview for DSM-IV (SCID), the HRS-D17 (17-item Hamilton Rating Scale for Depression) and the QIDS-C16 (Quick Inventory of Depressive Symptomatology) and reviewed the inclusion/exclusion criteria. After review with the Research Clinician (RC) and when appropriate, after a negative pregnancy test, the SC invited the subject to participate in the study if entry criteria were met. If the subject agreed, he/she received treatment according to the study protocol. Treatment schedule. Participants selected for inclusion in the study were seen at weeks 0, 4, and 8. The appropriate treatment antidepressant (citalopram or escitalopram) was determined following discussion between the patient and RC concerning medical, financial, and insurance considerations. The study medication citalopram was given in tablet or liquid form, depending on the patient's need. A fixed-flexible dose schedule was employed with the possibility of a dose escalation. In particular, the initial dose of citalopram was 20 mg and escitalopram was 10 mg. This dose was increased to 40 mg and 20 mg, respectively, after 4 weeks if the subject had a score of >/= 6 on the QIDS-C16 and the RC concluded that the subject could tolerate the increase. If necessary, dose was reduced at the discretion of the RC. The importance of medication compliance was discussed with the subject, and subjects were asked to bring in the medication at each visit for the purpose of counting remaining pills to assess compliance. Participants may have had an abbreviated course of therapy if they were clearly intolerant of the drug or failed to respond to a 40 mg dose of citalopram or a 20 mg dose of escitalopram. They then received standard treatment from a primary physician or psychiatrist with different medications and/or therapeutic modalities (e.g. psychotherapy). Special attention was given to subjects with suicidal ideation. Those subjects were offered additional appointments as needed to ensure their safety. Participants were also contacted by telephone at week 1 and week 2 by the Clinical Nurse Specialist (CNS) or other study personnel to ensure medication compliance and patient safety. The CNS was supervised by the study psychiatrist and principal investigator and deferred to the physician's judgment as needed. The SC was trained to notify the RC at the time of the clinical visits or the phone interviews if the subject endorsed any suicidal ideation or plans. For those subjects who were unable to attend the follow-up visits as scheduled, the patient was contacted by phone to complete the HRS-D17/QIDS-C16. The participant was also asked to return to the clinic to complete a final blood draw for the study. Adjunctive and concurrent medications. Adjunctive treatments were those used to manage transient associated symptoms (e.g., insomnia) or transient or longer-term medication side effects (e.g., sexual dysfunction). Adjunctive treatments that were not used to treat depression were allowed during the study. However, drugs in the exclusion criteria for this study were not used as adjunctive therapies. Subjects were allowed to participate in the study while receiving concurrent medications for general medical conditions as long as there was no contraindication to use while taking citalopram or escitalopram. Drug efficacy phenotype evaluation. The change in QIDS-C16 score and change in HRS-D17 score were used as the major research outcome measures to assess drug response. At each clinical research visit, the RC also completed the Clinician Global Impression of Improvement (CGI-I) scale. For the primary genome-wide association analyses, dichotomous outcome variables, remission and response, were defined based on the QIDS-C16 last visit score. Remission was defined as a QIDS-C16 score of 5 or less, while response was defined as at least a 50% reduction in the QIDS-C16 score from baseline. Specimen collection. Specimen collections were obtained by the General Clinical Research Center (GCRC) at St. Mary's Hospital (SMH). Subjects had blood drawn on an outpatient basis at GCRC-SMH on three separate occasions - baseline (0), week 4 and week 8 visits. Prior to sample collection, date and time of last dose of study medication was gathered at the 4 and 8 week visits. For those patients who were unable to complete the regularly scheduled visits, a dismissal blood draw was requested. GCRC then shipped samples to Mayo Clinic's Biospecimens Accessioning and Processing Lab. Genotyping and biomarker assays. DNA from 529 patients was genotyped by the RIKEN Center for Genomic Medicine (Yokohama, Japan) using Illumina Human610-Quad BeadChips (Illumina, San Diego, CA). The study also included the determination of plasma drug levels at weeks 4 and 8, and metabolite levels at baseline and weeks 4 and 8 for a subset of subjects. The primary genome-wide association study based on these data, which includes a summary of the protocol, subject description, selection of subjects for genetic analyses, and definitions of clinical outcomes, was published in the Pharmacogenomics Journal (Ji et al., 2012).

  Study phs000670

• Bogalusa Heart Study (BHS-BioLINCC)

  Objectives: To investigate the early natural history of cardiovascular disease in a cohort of children and young adults in a biracial, semirural community.Background: The Bogalusa Heart Study has been a long-term epidemiologic study. The investigators have identified and followed black and white participants for nearly 40 years, and have described the incidence and prevalence of biologic and behavioral cardiovascular disease risk factors from childhood through adulthood. Their participation has enabled the study to not only document differences between males and females, but also between blacks and whites. The results from the Bogalusa Heart Study have clearly documented that the genesis of atherosclerosis has its basis in childhood, and that prevention can and must begin at the early ages.The Bogalusa Heart Study had been funded over the years by the Specialized Centers of Research (SCOR) Program. The SCOR program was initiated by NHLBI in 1970 to expedite the development and application of new knowledge essential for improved diagnosis, treatment, and prevention of arteriosclerosis, hypertension, pulmonary disease, and thrombosis. In 1984 a Demonstration and Education Component was added to the parent SCOR of the Bogalusa Heart Study in order to translate the experience gained in epidemiological studies into an intervention study designed to retard the development of cardiovascular risk factors in children. Beginning in 1997, the study was supported by the cooperative agreement mechanism. Participants: The Bogalusa dataset includes 11,796 participants that attended at least one of seven cross-sectional pediatric exams and/or the 1995-96 adult examination. Subjects ranged in age from 3 to 20 years at the pediatric exams and 20-37 at the time of the adult exam. Approximately 6,000 have more than one examination constituting a dynamic cohort. Design: The initial survey in 1973-1974 was restricted to children ages 2 1/2 to 14. A physical examination was conducted and information was collected on anthropometric data, hemoglobin, blood pressure, serum lipids, and health history. Over 3,500 children participated. The second cross-sectional survey of 1976-1977 and subsequent surveys expanded the eligible population to include all children ages 5-17 years. The second survey of over 4,000 children also included information on salt intake, smoking, health beliefs, and attitudes, and for girls ages 8-17, menstrual history and oral contraceptive use. The third survey of over 3,500 participants in 1978-1979 also collected anthropometric measurements on skinfold thickness and two measurements of heart rate. The fourth survey of over 3,300 participants in 1981-1982 added data on alcohol use, Type A behavior, peer networks and dieting habits.The Bogalusa Heart Study continued to use a cross-sectional and longitudinal design with the general cross-sectional survey of approximately 3,700 Bogalusa children ages five to seventeen in 1988-1989 in the sixth screen and additional longitudinal studies to recall children in defined subgroups for more intensive evaluation. Half of the 12,000 participants screened since 1973 had been studied three or more times. The Post High School Study examined young adults ages 21 through 30 who previously were examined as children ages five through fourteen in the first Bogalusa Heart Study screening in 1973-1974. The population included approximately 4,603 young adults originally screened and any other children or adolescents examined for the first time in any subsequent surveys. The cardiovascular phenotypes include obesity, blood pressure, lipids, lipoproteins, apoproteins, homocysteine, glucose-insulin, fibrinogen, plasminogen activator inhibitor-1 and von Willebrand Factor. Environmental risk factors consist of sociodemographic characteristics, tobacco and alcohol use, oral contraception, physical activity, cognitive and physical function, and quality of sleep and diet. Subclinical morbidity includes echo-Doppler measurements of cardiac-carotid structure and function.

  Study phs004173

• How to upload GPG files

  Uploading files Users that holds an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. In order to manage the available resources, we enforce a limit of 10Tb per submission account at any one time. Please do not exceed this limit. INBOX FTP The FTP is only compatible with files encrypted using the EGACryptor tool Before uploading Once your submission files have been prepared using the EGAryptor, the resulting encrypted files and associated md5sum files can be uploaded to your submission account using Aspera or FTP. The EGA is a shared, public service with limited resources. In order to manage the available resources, EGA submission boxes should not exceed 8Tb in size, and cannot exceed 12Tb. If you are approaching this limit please contact contact EGA Helpdesk so that we can advise on how to register the associated metadata and trigger the archiving of files, so that you can continue with your submission. If we note that your submission account increases above 10Tb on a consistent base your password will be changed until metadata is associated. Aspera Download Aspera Using Aspera FTP FTP windows FTP Linux / Unix FTP client (Filezilla) FTP and TLS Troubleshooting Troubleshooting Aspera Download Aspera is a commercial file transfer protocol that may provide faster transfer speeds than ftp especially over longer distances. The Aspera ascp command line client. Please select Aspera Connect. The ascp command line client is distributed as part of the aspera connect highperformance transfer browser plugin and is free to use, without registration. The minimum required version of the IBM Aspera Cli is V4. Further instructions. Using the Aspera ascp command line program The location of the ascp program in the filesystem: Mac: on the desktop go cd /Applications/Aspera\ Connect.app/Contents/Resources/ there you'll see the command line utilities where you're going to use ascp. Windows: the downloaded files are a bit hidden. For instance, in Windows 7 the ascp.exe is located in the users home directory in: AppData\Local\Programs\Aspera\Aspera Connect\bin\ascp.exe Linux: should be in your user's home directory, cd /home/username/.aspera/connect/bin/ there you'll see the command line utilities where you're going to use ascp. Your command should look similar to this: ascp -P33001 -O33001 -QT -l300M -L- /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path If you wish to upload several files without being requested the password, please use the below command : ASPERA_SCP_PASS=ega-box-password ascp -P33001 -O33001 -QT -l300M /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path/ Explanation of parameters l300M option sets the upload speed limit to 30MB/s. You may wish to lower this value to increase the reliability of the transfer. L option is for printing logs out while transferring files to upload can be a file mask (e.g. '/homes/submitter/*.srf) or a list of files. ega-box-N is your submission account login. Add k2 switch for transfer restarts Check the command line transfer usage for more configuration details. Using FTP to upload your prepared files Use your preferred ftp client. For example, lftp is a popular choice for Linux and Mac users. Use binary mode for file transfers. Use ftp.ega.ebi.ac.uk as the target host. Login with your ega-box username and password. Upload files to your private ega-box upload area. Depending on your network setting you might wish to start FTP in passive or active mode. Using default FTP command line client in Windows Start the command line interpreter: press WinR, type cmd, hit enter Enter ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Use exit command to exit the command line interpreter. Using default FTP command line client in Linux / Unix Open a terminal and type ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Using FTP client FileZilla We recommend the use of FileZilla, a free FTP client . FileZilla is open source software distributed free of charge under the terms of the GNU General Public License. Use the following connection details (File - Site Manager) and add yoursubmission account username and password: Using FTP client FileZilla Select the files you wish to upload and then select upload: Using FTP client Filezilla Using LFTP with TLS We recommend the following to force the use of a secure connection. lftp > set ftp:ssl-force yes We also recommend setting the following for not encrypting the bulk data itself for performance reasons (theauthentication will still be encrypted): lftp > ftp:ssl-protect-data no In order to verify the certificate,the recommended way would be to use the CA certificates from your machine. To do that use this command in lftp adjusting the path to your ca-certificates location. lftp > ssl:ca-file "/etc/ssl/certs/ca-certificates.crt" If that is not possible or certificates are old and you can't update them, you can download the certificates needed from Quo Vadis Digital Repository The two certificates to download in PEM format are: QuoVadis Root CA2 G3 QuoVadis EV SSL ICA G3 Then you can concatenate them in a file one after the other and save it as lftp-certificates.pem Once this is done you have to point the ssl:ca-file variable to the path lftp > set ssl:ca-file "/path/to/lftp-certificates.pem" Also note that you can save this configuration at ~/.lftp/rc Another option is to download the certificates and add them to the ca-certificates of your machine. For example: In RHEL7 and cenots and others box the process to add the certificates globally is: Download the two certificates (in PEM or DER format, doesn't matter) and save them to "/etc/pki/ca-trust/source/anchors/" Run "update-ca-trust extract" Another less secure option is to turn off certificate verification with the following command: lftp > set ssl:verify-certificate false Troubleshooting If you are having problems with Aspera connection timeouts, it can be down to either one of the following. Transfers cannot start the connection fails instantly. Ensure that TCP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewall. The connection is made, transfers are started, but 0 bytes (0%) are uploaded for each file. Ensure that UDP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewal

  Documentation submission/data/uploading-files/ftp

• Patient-derived tumor organoids for personalized medicine in a rare case of hepatocellular carcinoma with neuroendocrine differentiation.

  Dac EGAC00001002458

• Analysis of recent shared ancestry in a familial cohort identifies coding and noncoding autism spectrum disorder variants

  Dac EGAC00001002558

• Single cell transcriptomics of hESC-derived midbrain dopaminergic neurons generated by a new human development-based protocol

  Dac EGAC00001002707

• A population specific reference genome defined from the analysis of 153 United Arab Emirates nationals.

  Study EGAS00001004537

• Oncolytic virotherapy mediated anti-tumor response in primary cutaneous B-cell lymphoma: a single-cell perspective

  Study EGAS00001004904

• Transcriptome Analysis Offers a Comprehensive Illustration of the Genetic Background of Pediatric Acute Myeloid Leukemia

  Study EGAS00001003701

• High coverage sequencing of a single sample can account for the problem of intratumor heterogeneity

  Study EGAS00001004200

• : Centromeric cohesion failure invokes a conserved choreography of chromosomal mis-segregations in pancreatic neuroendocrine tumours

  Study EGAS00001004239

• IG-MYC rearrangement defines a high-risk subgroup of B-cell precursor acute lymphoblastic leukaemia

  Study EGAS00001005111

• A renal cell carcinoma tumorgraft platform to advance precision medicine

  Study EGAS00001005516