Amplytica: Bringing Microbial Ecology To The Cloud.
Bibliographic record
Abstract
Poster for Canadian Society of Microbiologists Annual Conference 2017. Amplytica: Bringing microbial ecology to the cloud. Abstract The Amplytica Cloud Platform (ACP) is a software system for building large scale bioinformatics applications on commercial cloud computing infrastructure with a focus on microbial ecology workloads. It makes use of emerging open source cloud technologies such as Docker, Salt-Cloud, RabbitMQ, Binary Large Object (BLOB) stores such as Amazon S3 and cloud databases such as Heroku PostgreSQL. The platform is designed to be distributed across many servers which do not need to be in a cluster or even on the same cloud provider. Components can even be hosted on-site to utilize existing hardware or for data security purposes. They are also stateless and no sequence data is stored inside them allowing for failure at any time with minimal data loss. Since the system is distributed, components can be turned on and off on demand allowing end users to pay for only individual bioinformatics processing jobs rather than for longterm servers. In a microbial ecology context, ACP processing components wrap the QIIME microbial ecology pipeline allowing it to cluster OTUs in a closed, or in the future, open-reference guided fashion on high-RAM cloud virtual machines. The platform is targeted towards high throughput applications such as bioreactor monitoring and optimization, personalized medicine and sequencing centres with the ability to process, store and organized hundreds of samples per month. The platform also has facilities for sequence quality filtering and quality control, compressed storage of sequence data, project and sample management and metadata capture and integration. Unlike other competing platforms, ACP can be hosted by Canadian cloud providers or private cloud and will support for multiple sequencer vendors in the near future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.089 | 0.076 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".