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• A collection of 142 samples used to evaluate the sensitivity and specificity of small-scale variation detection in exome-capture data with a particular focus on indel detection.

  The dataset includes exome sequencing data and 704 Sanger sequencing validations for the 142 samples. The 704 Sanger sequencing validations are comprised of 416 variants and 288 sites known to be negative, including 293 indels and 247 negative indel sites. DNA libraries were prepared from genomic DNA using the Illumina TruSeq sample preparation kit. DNA was fragmented using Covaris technology and the libraries were prepared without gel size selection. Target enrichment was performed in pools of six libraries using the Illumina TruSeq Exome Enrichment kit. The captured DNA libraries were PCR amplified using the supplied paired-end PCR primers. Sequencing was performed with an Illumina HiSeq2000 (v3 flow cell, one pool per lane) generating 2x101-bp reads. Technology – Illumina HiSeq 2000

  Study EGAS00001001332

• Error-corrected sequencing of 26 driver genes

  387 swab samples were sequenced. The bam files contain consensus reads.

  Dataset EGAD50000000079

• Gut metagenomic data of 2,338 Pinggu adults

  Here we exploited a Han Chinese population-based cohort with extensive host metadata established in the Pinggu (PG) district of Beijing, and investigated gut microbiota from 2,338 adults (26-76 years) by metagenomic sequencing, revealing associations of the gut microbiota with sex, sex hormones, age, and a number of clinical and metabolic parameters.

  Study EGAS00001004820

• Adolescent Idiopathic Scoliosis (AIS) 1000 Exomes Study

  Adolescent idiopathic scoliosis (AIS) is the most common pediatric spinal deformity. Although the Bracing in AIS Trial (BrAIST) recently demonstrated the effectiveness of bracing for preventing scoliosis progression in some patients, more than 20,000 children undergo major spinal fusion surgery at an annual cost of $3 billion. Spinal fusion surgery is a major operation with considerable risks and complications. Accurate methods of predicting curve progression are needed to develop personalized prevention strategies for those at high risk and to eliminate screening and treatment of those at low risk of progression. Previously identified risk factors for scoliosis curve progression include sex, age of onset, curve type, and presence of an underlying disorder. However, currently available algorithms for predicting AIS curve progression are inaccurate, possibly because the role of genetic factors has been largely unexplored. Because there is little a priori knowledge of the genetic variants involved in AIS pathology, an unbiased genome-wide approach is likely to provide the best opportunity to comprehensively identify disease-associated genes. This is a multicenter exome sequencing study of extreme cases with severe scoliosis.

  Study phs001677

• Anaplastic meingioma methylation

  Methylation of anaplastic meningiona samples

  Dataset EGAD00010001629

• All you need to know about our new DAC Portal v2

  An updated version to boost the management of DACs (Data Access Committees), policies, and data access requests. In September of 2023, we launched new services for all EGA users, including a DAC Portal. Since then, our team has been working to refine it and improve the user experience. The version 2 of our DAC Portal is now ready, with a range of new features aimed at streamlining your workflow and boosting productivity. If you are interested in learning more about the latest version of our DAC Portal, then you are in the right place! In this post, you will find information about the implemented features and more. The full documentation is available on our website. Moreover, we highly recommend exploring the DAC Portal through the Take the Tour. This said, let's get cracking! Customise your data access requests table This table offers the possibility to tailor information about the data access requests by making visible or hiding different columns regarding the Dataset Persistent Identifier, the Organisation or the requestor's name s, among others. You can choose which columns to see, depending on your needs and interests. Additionally, we have implemented two new features columns with two new functionalities, DAC Comment and Expiration Date. You can find more details in the following sections! In this table, all data access requests are displayed by default. With the latest version of the Portal, you can now apply filters, combine them, and save them for future use. Leave internal comments on pending requests and visible to all the DAC Members We understand that many Data Access Committees (DACs) cannot instantly approve or deny data access requests. Often, there's an intermediary phase where discussions and the signing of legal documents are necessary. To facilitate this process, we've introduced the DAC comment feature! When you navigate to the DAC column, you'll find two tabs: "User" and "DAC". Under "User", you'll see the message provided by the requester at the time of requesting access to the data. As a DAC member/admin, you can then add an internal comment in the "DAC" tab. These comments are visible only to DAC members/admins, ensuring confidentiality. Requesters won't have access to DAC comments. By adding a DAC comment to a pending request, we'll know that you've begun reviewing it. This allows you to filter pending requests without DAC comments to focus on genuinely new requests, and it prevents you from receiving email notifications about ongoing data requests. Set expiration dates for permissions and revoke them automatically The latest update to the DAC Portal introduces a new feature allowing you to set expiration dates for new access requests. For instance, if you anticipate approving a data access request but only wish to grant access for a limited time, you can now specify an expiration date for the granted permissions. Once this date is reached, the permissions will automatically be revoked. By default, permissions are granted indefinitely without an expiration date. However, if you ever need to revoke permissions, you can easily do so from the History page. Audit your metadata objects Managing and auditing your metadata objects will be easier from now on as the new DAC Portal enhances and facilitates your role as a Data Controller. This is possible thanks to the tables available in each of the primary tabs visible on the homepage: DACs, Policies and Datasets. Starting with DACs, this section allows to check the policies linked to a specific DAC. In Policies, you can obtain a list of the datasets connected to a particular policy. In the Dataset tab, it is possible to group datasets by either DAC or Policy, as well as the release status. Now you have the option of deprecating metadata objects that you don’t want to use anymore, or that were created by mistake. You will find the deprecate button on the EDIT page of the DAC, as well as for registered policies. You can find more information here. What's neat about this feature is that once a DAC or policy is deprecated, it becomes hidden within the DAC Portal. This ensures that you only see information that is consistently up to date! Define your preferences regarding email notifications For pending requests, you will be able to decide how often you want to get the notifications: daily, weekly or fortnightly. Remember, you will not receive email notifications for pending requests with a DAC comment! Furthermore, you have the flexibility to decide which notifications you wish to receive, such as invitations to join a new DAC or notifications regarding DAC acceptance by the Helpdesk, among other options. Check the User preference to see the complete list of available notification settings! What’s next: incoming DAC Portal API In addition to the new DAC Portal, we are excited to announce the release of the DAC API! This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can consult the DAC API specification. This post was written by Aina Jené and Ana T. Alonso.

  Blog new-dac-portal-v2

• Colorectal adenomas, carcinomas and adjacent normal NKI-AvL TGO series NGS-ProToCol

  Cancer is caused by changes in the genome of cells, resulting in aberrant transcripts and proteins. In order to understand why normal cells become cancer cells and how we can identify and destroy them, the CTMM-NGS-ProToCol project aimed to understand prostate and colorectal cancer through DNA and RNA molecular profiling, to further improve diagnosis, prognosis and treatment.The state-of-the-art within the field of genome sequencing makes it possible to identify all the modifications in our genome that encode for RNA and of the transcripts themselves. The latest development in RNA sequencing is that if one does not select for polyA RNA, but first removes rRNA from the total RNA and then performs ‘random’ RNA sequencing, 30-60% of the reads are intronic. Since about 40% of the genome consists of genes (and only 1.6% is exonic), deep RNA sequencing will cover not only the exons, but also a large portion of the introns and therefore much of the genome. The power of the combined sequencing of genome and transcriptome is the full view of copy number aberrations (CNA), exon mutations and the consequence and relevance of the mutations at the RNA level in the form of differential gene expression, alternative splicing, alternative promoter usage, novel transcripts, fusion transcripts, read-through transcripts and mutations. For the colorectal (tumor) tissue dataset, we applied molecular profiling to 30 colorectal adenomas, 30 colorectal carcinomas and 18 normal adjacent colon tissues.

  Study EGAS00001002854

• High-Throughput LINE-1 Retrotransposon Discovery in Humans

  The aim of this study is to discover LINE-1 (L1) insertion sites present in humans that are absent from the reference genome sequence. We use the distinguishing nucleotide characteristics of human-specific L1 elements to resequence the L1 3' flanking regions thus locating the L1 insertion sites whether or not they are present in the reference genome assembly. In doing so, we have uncovered a higher than expected number of non-reference L1 insertions, an average of 152 insertions per individual. In addition, we find that any two individuals differ at an average of 285 sites with respect to presence or absence of L1 insertions in those sites, and use this to estimate the rate of retrotransposition in humans at 1 event per 140 live births (95% c.i. 1/95 to 1/270). We show that individual 'L1 profiles' recapitulate genetic ancestry as expected, and find that dimorphic insertions are more frequent in introns relative to fixed insertions.

  Study phs000273

• Expression data from the Neo-CheckRay trial

  ER+/HER2- early breast cancer were treated with neoadjuvant immune-modulating SBRT and anti-CD73. The RNAseq dataset contains fastq files for 234 samples corresponding to 134 patients. There are 208 breast cancer samples, taken at screening (N=136) or week 6 (N=76). In addition, there are 26 lymph node samples taken at screening.

  Dataset EGAD50000002552

• H3Africa H3AChipDesign Phenotype

  Basic phenotypic data (country, ethnicity and sex) for 348 samples of the H3Africa Chip Design Study. Divided into 8 datasets of 41 samples from Zambia, 24 samples from Cameroon, 50 samples from Mali, 26 samples from Cameroon, 49 samples from Nigeria, 48 samples from Botswana, 50 samples from Benin, 60 samples from Burkina Faso and Ghana.

  Dataset EGAD00001005310