P rogress in laboratory automation depends not only on automating the physical aspects of scientific experimentation, but also on the intellectual aspects. We present the conceptual design, implementation, and our user-experience of ''Adam,'' which uses machine intelligence to autonomously investigate the function of genes in the yeast Saccharomyces cerevisiae. These investigations involve cycles of hypothesis formation, design of experiments to test these hypotheses, physical execution of the experiments using laboratory automation, and the analysis of the results. The physical execution of the experiments involves growing specific yeast strains in specific media and measuring growth curves. Hundreds of such experiments can be executed daily without human intervention. We believe Adam to be the first machine to have autonomously discovered novel scientific knowledge. ( JALA 2010;15:33-40) INTRODUCTIONWe wish to automate all aspects of laboratory science, not just the physical experimentation, but also the intellectual aspects of hypothesis formation, experiment planning, and results analysis. A ''Robot Scientist'' is a physically implemented robotic system that applies techniques from artificial intelligence (AI) to execute cycles of automated scientific experimentation. 1 This contrasts with standard laboratory automation that normally focuses on just the physical aspects of experimentation. Our Robot Scientist ''Adam'' executes, with minimal human intervention, a complex combination of operations on yeast cell cultures at medium to high throughput and, moreover, is capable of modifying those operations according to the behavior of the organisms. 2 Again, this contrasts with standard laboratory automation that is normally characterized by medium-or highthroughput execution of a linear sequence of a relatively small number of different operations. 3e5 Automating Scientific DiscoveryAutomation has been integral to many of the changes in human society since the 19th century. The advent of computer science in the mid-20th century has made practical the idea of automating aspects of scientific discovery. Computers were initially used to automate simple linear processes, for example, to collect and process laboratory instrument data, perform astronomical calculations, and create ballistic tables.Later, AI began to be used to automate aspects of planning experiments and analyzing results. Meta-DENDRAL, developed in the 1960s, was the first automated system for scientific hypothesis generation. 9 and IDS 10 were all impressive examples of automated data-driven discovery systems that could discover scientific laws as algebraic equations. A more recent example uses iterative cycles of algorithmic correlation to distil natural laws of geometric and momentum conservation, using data captured from the motion-tracking studies of a range of simple and complex oscillators and pendula. 11 However, none of the systems described fully ''closes the loop;'' they either do not collect their own data, or do not use analyzed res...

Traditional 2nd generation sequencing strategies have significantly reduced the cost of sequencing the human genome and provide flexibility to query specific gene panels, the whole exome, or the whole genome. However, these methodologies are based on short reads which limit their ability to phase/haplotype over long genomic distances, accurately map reads between highly homologous regions (e.g., genes vs. pseudogenes), and robustly detect particular types of structural variants (e.g., inversions and translocations). Advances in microfluidics technology and precision reagent delivery allow long-range information to be rescued and preserved through the use of the 10x Genomics Chromium platform. Each input DNA fragment (~40-200kb) is partitioned into a gel-bead in emulsion (GEM), and subsequent biochemistry generates mini-libraries of NGS-ready molecules tagged with a barcode unique for each GEM. Thus, long-range context is achieved by linking short reads sharing the same barcode, and contiguity is established because they were derived from the same input fragment. Importantly, the barcoded mini-libraries are compatible with short-read sequencers and can be implemented as an add-on to existing sequencing infrastructures. Here we describe and demonstrate how the user-friendly and uniquely-tuned liquid handling capabilities of the PerkinElmer Sciclone® NGSx Workstation interface with the 10x Genomics chip to successfully automate the Chromium Genome workflow. We show the preservation of intact genomic DNA during automated library preparation and demonstrate that these libraries have comparable quality to those generated by manual preparation. Following Chromium partitioning, mini-library generation, and pooling of all the GEM mini-libraries, samples were processed again on the Sciclone using a previously established automated workflow for exome/panel target capture using Agilent SureSelectTM baits. This end-to-end automated workflow was used to generate Linked-Read whole exome data on samples with unresolved structural rearrangements and targeted Linked Reads in a Lynch syndrome gene, PMS2. Linked Reads enable us to 1) fine-map structural rearrangements detected by karyotyping and 2) resolve variants in PMS2 versus those in its homologous pseudogene, PMS2CL, without invoking non-NGS methods such as MLPA or long-range PCR. The benefits of automation are essential to the scale-up of high-throughput projects by removing manual variability and increasing efficiency. This partnership offers a unique workflow solution that enables exome and panel-based Linked-Read sequencing at scale. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Renata Pellegrino, Michael Benway, Paulina Kocjan, Andrew Price, Charlly Kao, Brian A. Gerwe, Adrian Fehr, Fernanda Mafra, James Garifallou, Hakon Hakonarson. High-throughput automation of the 10x Genomics® Chromium™ workflow for linked-read whole exome sequencing and a targeted lynch syndrome panel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5353. doi:10.1158/1538-7445.AM2017-5353