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1 |
Genome Annotator Light (GAL): A Docker-based package for genome analysis and visualization
نور حاشیه نویسی ژنوم (GAL): یک بسته مبتنی بر داکر برای تجزیه و تحلیل ژنوم و تجسم ژنوم-2020 Next generation sequencing techniques produce enormous data but its analysis and visualization remains a big
challenge. To address this, we have developed Genome Annotator Light(GAL), a Docker based package for
genome analysis and data visualization. GAL integrated several existing tools and in-house programs inside a
Docker Container for systematic analysis and visualization of genomes through web browser. GAL takes varieties
of input types ranging from raw Fasta files to fully annotated files, processes them through a standard annotation
pipeline and visualizes on a web browser. Comparative genomic analysis is performed automatically within a
given taxonomic class. GAL creates interactive genome browser with clickable genomic feature tracks; local
BLAST-able database; query page, on-fly downstream data analysis using EMBOSS etc. Overall, GAL is an extremely
convenient, portable and platform independent. Fully integrated web-resources can be easily created
and deployed, e.g. www.eumicrobedb.org/cglab, for our in-house genomes. GAL is freely available at https://
hub.docker.com/u/cglabiicb/. Keywords: Genome analysis | Genome browser | Docker | EMBOSS | Visualization |
مقاله انگلیسی |
2 |
Artificial intelligence (AI) and big data in cancer and precision oncology
هوش مصنوعی و داده های بزرگ در سرطان و انکولوژی دقیق -2020 Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare
sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and
time-effective manner. Clinical oncology and research are reaping the benefits of AI. The burden of cancer
is a global phenomenon. Efforts to reduce mortality rates requires early diagnosis for effective therapeutic
interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative
to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance
treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these
demands, has revolutionised the future of precision oncology. NGS offers several clinical applications that
are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging,
accurate prognosis, biomarker identification and identification of therapeutic targets for novel drug discovery.
NGS generates large datasets that demand specialised bioinformatics resources to analyse the
data that is relevant and clinically significant. Through these applications of AI, cancer diagnostics and
prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images.
Regardless of the improvements in technology, AI has some challenges and limitations, and the clinical
application of NGS remains to be validated. By continuing to enhance the progression of innovation
and technology, the future of AI and precision oncology show great promise. Keywords: Artificial intelligence | Machine learning | Deep learning | Big datasets | Precision oncology | NGS and bioinformatics | Medical imaging | Digital pathology | Diagnosis | Treatment | Prognosis and drug discovery |
مقاله انگلیسی |
3 |
Micro Sequence Identification of DNA Data Using Pattern Mining Techniques
شناسایی میکروارگانیسم داده های DNA با استفاده از تکنیک های کاوش الگو-2018 A rapid development of NGS(Next generation Sequencing) technologies can be able to produce large amounts of sequence
data,which is leading in to a wide range of new applications. Sequence matches between a translated nucleotide sequence and a
well known biological protein can able to provide a strong evidence for the presence of homologous coding region, and such
similarities often be identified even between a distantly related genes. Common techniques often restrict indels in the alignment
to improve time,speed, whereas more Flexible aligners are too slow for large-scale applications. Moreover, many current aligners
are becoming inefficient as generated reads grow ever larger. To perform such high dimensional process, it requires a special
hardware implementation & designing, such implementation can also increases a complexity of efficiency and hardware. The
Field Programmable Gate Array is the well-known to design and we propose a high efficient algorithm for sequence detection in
any of bioinformatics data. Unlike previous methods, the proposed pattern matching algorithm can identifies the sequence of
each factor on the basis of their occurrences. This method can computes the multi-level similarity measure with an available
sequences. Based on the multi-level sequence similarity measure computed a single sequence of bioinformatics data can be
identified. The proposed method produces efficient result in sequence searching and detection and improves the hardware
utilization which in terms reduces the time complexity as well.
Keywords: Bioinformatics; Pattern Matching; Sequence Identification; Dynamic Programming; Hardware Acceleration |
مقاله انگلیسی |
4 |
parSRA: A framework for the parallel execution of short read aligners on compute clusters
parSRA: چارچوبی برای اجرای موازی aligners خواندن کوتاه بر روی خوشه های محاسبه-2017 The growth of next generation sequencing datasets poses as a challenge to the alignment of reads to
reference genomes in terms of both accuracy and speed. In this work we present parSRA, a parallel
framework to accelerate the execution of existing short read aligners on distributed-memory systems.
parSRA can be used to parallelize a variety of short read alignment tools installed in the system without any
modification to their source code. We show that our framework provides good scalability on a compute
cluster for accelerating the popular BWA-MEM and Bowtie2 aligners. On average, it is able to accelerate
sequence alignments on 16 64-core nodes (in total, 1024 cores) with speedup of 10.48 compared to the
original multithreaded tools running with 64 threads on one node. It is also faster and more scalable than
the pMap and BigBWA frameworks. Source code of parSRA in C++ and UPC++ running on Linux systems
with support for FUSE is freely available at https://sourceforge.net/projects/parsra/.
Keywords: Short read alignment | High performance computing | Multicore clusters | Bioinformatics | PGAS |
مقاله انگلیسی |
5 |
parSRA: A framework for the parallel execution of short read aligners on compute clusters
parSRA: چارچوبی برای اجرای موازی هماهنگ کنندگان خواندن کوتاه در خوشه های محاسبه ای-2017 The growth of next generation sequencing datasets poses as a challenge to the alignment of reads to
reference genomes in terms of both accuracy and speed. In this work we present parSRA, a parallel
framework to accelerate the execution of existing short read aligners on distributed-memory systems.
parSRA can be used to parallelize a variety of short read alignment tools installed in the system without any
modification to their source code. We show that our framework provides good scalability on a compute
cluster for accelerating the popular BWA-MEM and Bowtie2 aligners. On average, it is able to accelerate
sequence alignments on 16 64-core nodes (in total, 1024 cores) with speedup of 10.48 compared to the
original multithreaded tools running with 64 threads on one node. It is also faster and more scalable than
the pMap and BigBWA frameworks. Source code of parSRA in C++ and UPC++ running on Linux systems
with support for FUSE is freely available at https://sourceforge.net/projects/parsra/.
Keywords: Short read alignment | High performance computing | Multicore clusters | Bioinformatics | PGAS |
مقاله انگلیسی |
6 |
آموزش مشاوره ژنتیک :تربیت نیروی مشاوره ژنتیک برای عصر ژنوم
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 14 مشاوران ژنتیک سابقه ی طولانی در خصوص کار بر روی خط مقدم بالینی پیاده سازی تکنولوژی جدید ژنتیک را دارا هستند.توالی یابی ژنومی بدون استثناست. پیشرفت سریع فناوری های توالی یابی ژنومی ، اما نه محدود به رویکرد های توالی یابی نسل بعدی ، در میان همه ی زیرمجموعه های مشاوره ی ژنتیکی توجه را به تلاش مشاور ژنتیک در هردو سطح آموزشی مقدماتی و تکمیلی جلب می کند. عصر حاضر فرصتی فوق العاده را برای مشاورین فراهم می کند تا فعالانه در ساختن ژنومی قابل دسترس تر و جذب جمعیت در تصمیم گیری به منظور پذیرش توالی یابی و ترجمه موثر اطلاعات ژنومی برای ارتقاء سلامت و تندرستی شرکت کنند. در این گزارش ، ما دلایل این که چرا توالی یابی ژنومی توجه خاصی می طلبد را بررسی می کنیم و استراتژی هایی برای آموزش برنامه های تحصیلی و برنامه های آموزشی مدون به منظور پاسخ گویی به این نیاز را تعیین می کنیم.
کلمات کلیدی : ژنوم | توالی یابی نسل بعد | آموزش مشاوره ژنتیک | آموزش تکمیلی |
مقاله ترجمه شده |