NCGC participates in ICGC mutation calling benchmark study

1 Leave a comment on paragraph 1 0 [et_pb_section admin_label=”Seksjon” fullwidth=”on” specialty=”off”][et_pb_fullwidth_post_title admin_label=”Fullbreddes innleggstittel” title=”on” meta=”off” author=”on” date=”on” categories=”on” comments=”on” featured_image=”on” featured_placement=”background” parallax_effect=”off” parallax_method=”on” text_orientation=”right” text_color=”light” text_background=”on” text_bg_color=”rgba(21,150,137,0.9)” module_bg_color=”rgba(255,255,255,0)” title_font=”|on|||” title_font_size=”35px” title_all_caps=”off” use_border_color=”off” border_color=”#ffffff” border_style=”solid” module_id=”header-image-and-text”]

2 Leave a comment on paragraph 2 0  

3 Leave a comment on paragraph 3 0 [/et_pb_fullwidth_post_title][/et_pb_section][et_pb_section admin_label=”Seksjon” fullwidth=”off” specialty=”off”][et_pb_row admin_label=”Rad”][et_pb_column type=”2_3″][et_pb_text admin_label=”Tekst” background_layout=”light” text_orientation=”left” use_border_color=”off” border_color=”#ffffff” border_style=”solid”]

Published in Nature Communications

4 Leave a comment on paragraph 4 0  

There are numerous variations to the algorithms used for detection of somatic mutations in cancer based on next generation sequencing, and these have a profound effect on what mutations are detected and which artefacts appear. The International Cancer Genomics Consortium, including the NCGC Bioinformatics team, has benchmarked the procedure, and determined an optimized consensus pipeline which is now used for the NCGC studies. This is one of the central objectives of the NCGC platform, – to establish common, reliable procedures for somatic mutation scoring in patient samples, and lay the groundwork for standardized nation-wide diagnostic procedures in the Norwegian health service.

6 Leave a comment on paragraph 6 0 The study was initiated by a comparison performed by ICGC by letting 14 of the best bioinformatics teams identify mutations from the same whole genome tumour/normal data set using their best algorithms.

7 Leave a comment on paragraph 7 0

8 Leave a comment on paragraph 8 0 The figure shows the fractions of point mutation calls that were common among various numbers of groups, whereas larger mutations (indels) varied even more.

9 Leave a comment on paragraph 9 0 These data sets, and those in the published study, are much larger than those produced by NCGC, where only the genes (the exome, 60 mbp) are sequenced. However, the principles for mutation calling are quite the same. Although whole genome data are regarded as more even than exome data, the study determined that both blood and tumour should be sequenced to  coverage of 100x to score most mutations, whereas even deeper sequencing might be neccessary to obtain also mutations in smaller subclones of cancer cells.

10 Leave a comment on paragraph 10 0 You may see the paper here.

11 Leave a comment on paragraph 11 0 Read about the NCGC Bioinformatics effort.

12 Leave a comment on paragraph 12 0 [/et_pb_text][/et_pb_column][et_pb_column type=”1_3″][et_pb_sidebar admin_label=”Nyhetsarkiv og konferanser” orientation=”left” area=”primary” background_layout=”light” remove_border=”off”]

13 Leave a comment on paragraph 13 0  

14 Leave a comment on paragraph 14 0 [/et_pb_sidebar][/et_pb_column][/et_pb_row][/et_pb_section]