| 1. |
- Delhomme, Nicolas, et al.
(författare)
-
Serendipitous Meta-Transcriptomics : The Fungal Community of Norway Spruce (Picea abies)
- 2015
-
Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:9
-
Tidskriftsartikel (refereegranskat)abstract
- After performing de novo transcript assembly of >1 billion RNA-Sequencing reads obtained from 22 samples of different Norway spruce (Picea abies) tissues that were not surface sterilized, we found that assembled sequences captured a mix of plant, lichen, and fungal transcripts. The latter were likely expressed by endophytic and epiphytic symbionts, indicating that these organisms were present, alive, and metabolically active. Here, we show that these serendipitously sequenced transcripts need not be considered merely as contamination, as is common, but that they provide insight into the plant's phyllosphere. Notably, we could classify these transcripts as originating predominantly from Dothideomycetes and Leotiomycetes species, with functional annotation of gene families indicating active growth and metabolism, with particular regards to glucose intake and processing, as well as gene regulation.
|
|
| 2. |
- Haas, Brian J., et al.
(författare)
-
Genome sequence and analysis of the Irish potato famine pathogen Phytophthora infestans
- 2009
-
Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 461:7262, s. 393-398
-
Tidskriftsartikel (refereegranskat)abstract
- Phytophthora infestans is the most destructive pathogen of potato and a model organism for the oomycetes, a distinct lineage of fungus-like eukaryotes that are related to organisms such as brown algae and diatoms. As the agent of the Irish potato famine in the mid-nineteenth century, P. infestans has had a tremendous effect on human history, resulting in famine and population displacement(1). To this day, it affects world agriculture by causing the most destructive disease of potato, the fourth largest food crop and a critical alternative to the major cereal crops for feeding the world's population(1). Current annual worldwide potato crop losses due to late blight are conservatively estimated at $6.7 billion(2). Management of this devastating pathogen is challenged by its remarkable speed of adaptation to control strategies such as genetically resistant cultivars(3,4). Here we report the sequence of the P. infestans genome, which at similar to 240 megabases (Mb) is by far the largest and most complex genome sequenced so far in the chromalveolates. Its expansion results from a proliferation of repetitive DNA accounting for similar to 74% of the genome. Comparison with two other Phytophthora genomes showed rapid turnover and extensive expansion of specific families of secreted disease effector proteins, including many genes that are induced during infection or are predicted to have activities that alter host physiology. These fast-evolving effector genes are localized to highly dynamic and expanded regions of the P. infestans genome. This probably plays a crucial part in the rapid adaptability of the pathogen to host plants and underpins its evolutionary potential.
|
|
| 3. |
- Rivas-Carrillo, Salvador Daniel, et al.
(författare)
-
MindReader : Unsupervised Classification of Electroencephalographic Data
- 2023
-
Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 23:6, s. 2971-
-
Tidskriftsartikel (refereegranskat)abstract
- Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes interpretation time-consuming, resource-hungry, and overall an expensive process. Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data and optimizing the allocation of human resources towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader's predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use.
|
|
| 4. |
- Stathis, Dimitrios, 1989-, et al.
(författare)
-
Approximate Computing Applied to Bacterial Genome Identification using Self-Organizing Maps
- 2019
-
Ingår i: 2019 IEEE Computer Society Annual Symposium On VLSI (ISVLSI 2019). - : IEEE. - 9781728133911 ; , s. 562-569
-
Konferensbidrag (refereegranskat)abstract
- In this paper we explore the design space of a self-organizing map (SOM) used for rapid and accurate identification of bacterial genomes. This is an important health care problem because even in Europe, 70% of prescriptions for antibiotics is wrong. The SOM is trained on Next Generation Sequencing (NGS) data and is able to identify the exact strain of bacteria. This is in contrast to conventional methods that require genome assembly to identify the bacterial strain. SOM has been implemented as an synchoros VLSI design and shown to have 3-4 orders better computational efficiency compared to GPUs. To further lower the energy consumption, we exploit the robustness of SOM by successively lowering the resolution to gain further improvements in efficiency and lower the implementation cost without substantially sacrificing the accuracy. We do an in depth analysis of the reduction in resolution vs. loss in accuracy as the basis for designing a system with the lowest cost and acceptable accuracy using NGS data from samples containing multiple bacteria from the labs of one of the co-authors. The objective of this method is to design a bacterial recognition system for battery operated clinical use where the area, power and performance are of critical importance. We demonstrate that with 39% loss in accuracy in 12 hits and 1% in 16 bit representation can yield significant savings in energy and area.
|
|
| 5. |
- Yang, Yu, et al.
(författare)
-
RiBoSOM : Rapid bacterial genome identification using self-organizing map implemented on the synchoros SiLago platform
- 2018
-
Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450364942 ; , s. 105-114
-
Konferensbidrag (refereegranskat)abstract
- Artificial Neural Networks have been applied to many traditional machine learning applications in image and speech processing. More recently, ANNs have caught attention of the bioinformatics community for their ability to not only speed up by not having to assemble genomes but also work with imperfect data set with duplications. ANNs for bioinformatics also have the added attraction of better scaling for massive parallelism compared to traditional bioinformatics algorithms. In this paper, we have adapted Self-organizing Maps for rapid identification of bacterial genomes called BioSOM. BioSOM has been implemented on a design of two coarse grain reconfigurable fabrics customized for dense linear algebra and streaming scratchpad memory respectively. These fabrics are implemented in a novel synchoros VLSI design style that enables composition by abutment. The synchoricity empowers rapid and accurate synthesis from Matlab models to create near ASIC like efficient solution. This platform, called SiLago (Silicon Lego) is benchmarked against a GPU implementation. The SiLago implementation of BioSOMs in four different dimensions, 128, 256, 512 and 1024 Neurons, were trained for two E Coli strains of bacteria with 40K training vectors. The results of SiLago implementation were benchmarked against a GPU GTX 1070 implementation in the CUDA framework. The comparison reveals 4 to 140X speed up and 4 to 5 orders of improvement in energy-delay product compared to implementation on GPU. This extreme efficiency comes with the added benefit of automated generation of GDSII level design from Matlab by using the Synchoros VLSI design style.
|
|
