Background: SNP (Single Nucleotide Polymorphism) discovery is now routinely performed using high-throughput sequencing of reduced representation libraries. Our objective was to adapt 454 GS FLX based sequencing methodologies in order to obtain the largest possible dataset from two reduced representations libraries, produced by AFLP (Amplified Fragment Length Polymorphism) for genomic DNA, and EST (Expressed Sequence Tag) for the transcribed fraction of the genome.
Findings: The expressed fraction was obtained by preparing cDNA libraries without PCR amplification from quail embryo and brain. To optimize the information content for SNP analyses, libraries were prepared from individuals selected in three quail lines and each individual in the AFLP library was tagged. Sequencing runs produced 399,189 sequence reads from cDNA and 373,484 from genomic fragments, covering close to 250 Mb of sequence in total.
Conclusions: Both methods used to obtain reduced representations for high-throughput sequencing were successful after several improvements.The protocols may be used for several sequencing applications, such as de novo sequencing, tagged PCR fragments or long fragment sequencing of cDNA.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2919564 | PMC |
http://dx.doi.org/10.1186/1756-0500-3-214 | DOI Listing |
Sci Rep
December 2024
Translational Oncogenomics and Bioinformatics Lab, Center for Medical Biotechnology, VIB-UGent & CRIG, Technologiepark-Zwijnaarde 75, 9052, Ghent, Belgium.
Esophageal adenocarcinoma (EAC) is an aggressive cancer characterized by a high risk of relapse post-surgery. Current follow-up methods (serum carcinoembryonic antigen detection and PET-CT) lack sensitivity and reliability, necessitating a novel approach. Analyzing cell-free DNA (cfDNA) from blood plasma emerges as a promising avenue.
View Article and Find Full Text PDFSci Rep
December 2024
Henan University of Engineering, Zhengzhou, 451191, China.
Social media generates vast amounts of spatio-temporal sequential data. However, current methods often ignore the complex spatio-temporal correlations within these data. This oversight makes it difficult to fully capture the dynamic features of the data.
View Article and Find Full Text PDFSci Rep
December 2024
School of Construction Machinery, Shandong Jiaotong University, Jinan, 250023, China.
Injection molded parts are increasingly utilized across various industries due to their cost-effectiveness, lightweight nature, and durability. However, traditional defect detection methods for these parts often rely on manual visual inspection, which is inefficient, expensive, and prone to errors. To enhance the accuracy of defect detection in injection molded parts, a new method called MRB-YOLO, based on the YOLOv8 model, has been proposed.
View Article and Find Full Text PDFSci Rep
December 2024
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, 710072, Shanxi, China.
In real engineering scenarios, the complex and variable operating conditions of mechanical equipment lead to distributional differences between the collected fault data and the training data. This distribution difference can lead to the failure of deep learning-based diagnostic models. Extracting generalized diagnostic knowledge from the source domain in scenarios where the target domain is not visible is the key to solving this problem.
View Article and Find Full Text PDFIn unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. In response to this scenario, source-free domain transfer algorithm with reduced style sensitivity (SFDT-RSS) is designed. SFDT-RSS initially pre-trains the source domain model by using the generalization strategy and subsequently adapts the pre-trained model to target domain without accessing source data.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!