Santa Clara, CA, United States
Santa Clara, CA, United States

NextBio is a privately owned software company that provides a platform for drug companies and life science researchers to search, discover, and share knowledge across public and proprietary data. It was co-founded by Saeid Akhtari, Ilya Kupershmidt and Mostafa Ronaghi in 2004 and based in Cupertino, California, USA. The NextBio Platform is an ontology-based semantic framework that connects highly heterogeneous data and textual information. The semantic framework is based on gene, tissue, disease and compound ontologies. This framework contains information from different organisms, platforms, data types and research areas that is integrated into and correlated within a single searchable environment using proprietary algorithms. It provides a unified interface for researchers to formulate and test new hypotheses across vast collections of experimental data. According to the company, the enterprise version of the NextBio platform is being used in life science research and development and drug development by researchers and clinicians at: Merck Pharmaceutical, Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Celgene, Genzyme, Eli Lilly and Company, and Regeneron Pharmaceuticals. This enterprise version allows internal, proprietary data to be uploaded and integrated into the NextBio database of publicly available data. According to the company, scientists are using NextBio to improve their ability to identify relevant prognostic and predictive molecular signatures which are significant in their research.NextBio was a receiver of the Frost & Sullivan North American Life Sciences Customer Value Enhancement Award in 2008. Wikipedia.

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Patent
NextBio | Date: 2017-03-06

According to various embodiments, aspects of the invention provide a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from diverse sequencing technologies as well as different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure. The methods, systems and apparatuses described enable combining orthogonal types of data and available public knowledge to elucidate mechanisms governing normal development, disease progression, as well as susceptibility of individuals to disease or response to drug treatments.


Global Life Science Companies, Cancer Research Leaders, and Worldwide Dignitaries Celebrate the Opening of ProCan -- an International Centre for Cancer Research -- with Goal to Transform the Way that Cancer is Diagnosed and Treated SOUTH EASTON, MA--(Marketwired - Oct 26, 2016) - Pressure BioSciences, Inc. ( : PBIO) ("PBI" and the "Company"), a leader in the development and sale of broadly enabling pressure cycling technology ("PCT")-based sample preparation solutions to the large and growing worldwide life sciences industry, today announced its featured participation in the recent opening of The ACRF International Centre for the Proteome of Human Cancer ("ProCan"), located in newly renovated laboratory facilities at the Children's Medical Research Institute ("CMRI") near Sydney, Australia. In addition to PBI, ProCan and CMRI scientists, presentations were made by other invitees, including representatives from SCIEX, Ilumina, Beckman-Coulter, and NextBio, as well as from Dr. Tiannan Guo of Professor Ruedi Aebersold's lab at the world-renowned Institute of Molecular Systems Biology (Zurich, Switzerland). Professor Aebersold is generally considered to be one of the leading proteomics scientists in the world. ProCan expects to analyze a minimum of 10,000 cancer tumor samples per year over the next seven years with cutting-edge protein analysis instruments and other lab tools. It is anticipated that data from their studies will trigger discoveries illuminating causes of cancer, providing invaluable guidance on cancer treatment options and creating a new era in standard operating procedures applied in cancer testing laboratories worldwide. "ProCan is very simple in concept but massive in scale," said CMRI Director, Professor Roger Reddel, who is also a co-leader of ProCan. "We believe the results of ProCan will greatly improve the speed and accuracy of cancer diagnosis and provide clinicians an enhanced capability to choose the most effective treatment option for each individual patient's cancer and, importantly, to avoid those treatments that are likely to be unsuccessful. This will reduce treatment toxicity and improve cancer treatment outcomes in children and adults -- worldwide." (Source of quote plus more information on ProCan and CMRI can be found at http://www.cmri.org.au/News/Latest-News/ProCan-Officially-Opens-at-CMRI.) In June 2016, ProCan purchased the first three commercially released, next-generation PCT-based instruments, the Barocycler 2320EXTREME (the "2320EXT"). A short video introducing the 2320EXT can be viewed at https://youtu.be/xbO6Lp4VxwU. In their planned analysis of 70,000-plus tumor samples, ProCan will combine PBI's Barocycler 2320EXT system for sample preparation with SCIEX's SWATH data independent-acquisition mass spectrometry workflow on Triple TOF® 6600 Systems. The advantages of this method ("PCT-SWATH") have been the focus of scientific journal articles by Dr. Aebersold, Dr. Guo, and others over the past several years. SCIEX is a global leader in life science analytical technologies. In January 2016, PBI and SCIEX announced an exclusive, two-year, worldwide co-marketing agreement under which PBI and SCIEX will co-promote PBI's PCT systems with SCIEX's SWATH-based proteomics workflows. Dr. Alexander Lazarev, Vice President of R&D at PBI said: "We were honored when ProCan recognized and selected the PCT platform for the significant advantages in sample preparation that it affords. Reproducibility, speed, automation, and enhanced protein extraction and digestion are all critical elements in the preparation of samples for analysis. When these sample preparation attributes of PCT are combined with the leading quality of SCIEX mass spectrometers, we believe that ProCan and other users of PCT-SWATH will significantly increase their chances to discover new and potentially important biomarkers of cancer." Dr. Nate Lawrence, Vice President of Sales and Marketing for PBI, commented: "CMRI was recently named an official collaborator of the US National Cancer Institute's "Cancer Moonshot" initiative, whose goal is to accelerate what would normally take ten years of cancer research into completion in the next five years. We believe that the Cancer Moonshot initiative will strategically support and accelerate the field of precision (personalized) medicine through studies such as those planned at CMRI, which could lead to better identification, diagnosis, treatment, and prevention of cancer. We are proud to be a part of this very important and inspiring program." Dr. Lawrence continued: "Nearly $1 Billion in new funding is planned for cancer research in the current US fiscal year, a portion of which could support the expansion of additional "industrialized proteomics" labs worldwide, similar to ProCan. With the recognition that ProCan and other cancer research programs are already giving to our recently released, next-generation 2320EXT Barocycler system, combined with our planned salesforce expansion and our SCIEX co-marketing program, we believe we will see robustly increasing sales of our PCT product line in 2017 and beyond." Pressure BioSciences, Inc. ("PBI") ( : PBIO) develops, markets, and sells proprietary laboratory instrumentation and associated consumables to the estimated $6 billion life sciences sample preparation market. Our products are based on the unique properties of both constant (i.e., static) and alternating (i.e., pressure cycling technology, or PCT) hydrostatic pressure. PCT is a patented enabling technology platform that uses alternating cycles of hydrostatic pressure between ambient and ultra-high levels to safely and reproducibly control bio-molecular interactions. To date, we have installed over 260 PCT systems in approximately 160 sites worldwide. There are over 100 publications citing the advantages of the PCT platform over competitive methods, many from key opinion leaders. Our primary application development and sales efforts are in the biomarker discovery and forensics areas. Customers also use our products in other areas, such as drug discovery & design, bio-therapeutics characterization, soil & plant biology, vaccine development, histology, and forensic applications. Forward Looking Statements Statements contained in this press release regarding PBI's intentions, hopes, beliefs, expectations, or predictions of the future are "forward-looking'' statements within the meaning of the Private Securities Litigation Reform Act of 1995. These statements are based upon the Company's current expectations, forecasts, and assumptions that are subject to risks, uncertainties, and other factors that could cause actual outcomes and results to differ materially from those indicated by these forward-looking statements. These risks, uncertainties, and other factors include, but are not limited to, the risks and uncertainties discussed under the heading "Risk Factors" in the Company's Annual Report on Form 10-K for the year ended December 31, 2015, and other reports filed by the Company from time to time with the SEC. The Company undertakes no obligation to update any of the information included in this release, except as otherwise required by law. For more information about PBI and this press release, please click on the following website link: http://www.pressurebiosciences.com Please visit us on Facebook, LinkedIn, and Twitter


News Article | September 28, 2016
Site: www.nature.com

No statistical methods were used to predetermine sample size. Experiments were not randomized, and investigators were not blinded to allocation during experiments and outcome assessment. The 5′ and 3′ regions of homology in the targeting vector for Chd8 consisted of a 5.5-kb fragment spanning introns 4 and 10 and a 1.2-kb fragment of intron 13 and exon 14, respectively. The neomycin-resistance gene (neo) flanked by loxP sites was isolated from the plasmid pL2-Neo(2) (provided by D. R. Littman)31 and inserted downstream of exon 13 of Chd8. A loxP site was also inserted upstream of exon 11. Embryonic stem cell clones that had undergone homologous recombination were transfected with pMC-Cre (provided by D. R. Littman) to excise the loxP-neo cassette and target region. Mutant cells were then microinjected into C57BL/6 blastocysts, and the resulting chimaeras were mated with C57BL/6 mice. Offspring were backcrossed onto the C57BL/6J line for at least nine generations. Most experiments were performed with male littermates produced by WT × heterozygote (Chd8+/∆SL or Chd8+/∆L) crosses. All animals were maintained under specific pathogen–free conditions, and all experiments were approved by the animal ethics committee of Kyushu University. Complementary DNAs encoding human CHD8 and FOXP1, each tagged with the Flag epitope at its NH -terminus, were subcloned into pcDNA3 (Invitrogen). Rabbit polyclonal antibodies to pan-CHD8 were generated in-house and used for immunoblot analysis, and a rat monoclonal antibody to CHD8 was used for ChIP, immunoblot and immunoprecipitation. Anti-CHD8 monoclonal antibody was generated by the lymph-node method32 with the use of a fusion protein of mouse CHD8 and glutathione S-transferase (GST) synthesized in Escherichia coli strain BL21(DE3)pLys(S) (Novagen). Antibodies to CHD8 (A301-224A and A301-225A) were obtained from Bethyl Laboratories, those to REST (07-579 and 17-641) were from Merck Millipore, those to Flag (F3165) were from Sigma–Aldrich, and those to Hsp90 (610419, loading control) were from BD Biosciences. The specificity of in-house CHD8 antibodies was verified in this study, and that of other antibodies was verified in 1DegreeBio (http://1degreebio.org/) or our previous studies12, 13. HEK293T cells (human embryonic kidney cell line) were cultured under an atmosphere of 5% CO at 37 °C in DMEM (Invitrogen) supplemented with 10% fetal bovine serum (Invitrogen). The cells were transfected with vectors with the use of FuGENE6 (Roche). Cell lysis, immunoprecipitation and immunoblot analysis were performed as described previously33. Immunoblot signals were quantified with the use of ImageQuant TL software (GE Healthcare Life Sciences). Mouse embryonic fibroblasts (MEFs) were treated with cycloheximide (100 μg ml−1) to inhibit protein synthesis and harvested at the indicated times thereafter for immunoblot analysis. Total RNA (1 μg) isolated from mouse brain tissue with the use of Isogen (Nippon Gene) was subjected to reverse transcription (RT) with a QuantiTect Reverse Transcription Kit (Qiagen), and the resulting cDNA was subjected to quantitative PCR analysis with the use of SYBR Green PCR Master Mix and specific primers in a StepOnePlus Real-Time PCR System (Applied Biosystems). PCR primer sequences (sense and antisense, respectively) were as follows: Chd8 , 5′-CAGATGAGACACTTCTTTCATGAA-3′ and 5′-TTCTCCGCGCCCAACTCAC-3′; Chd8 , 5′-TCCCTTTTTGGTCATTGCTC-3′ and 5′-TTCAGCCTATGGGCTTCATC-3′; Rest, 5′-ACCGGGTCAGGATCTTCTCA-3′ and 5′-GCCCTGTTAGGGAAACCTCC-3′; and Rplp0, 5′-GGACCCGAGAAGACCTCCTT-3′ and 5′-GCACATCACTCAGAATTTCAATGG-3′. The amount of Chd8 or Rest mRNA was normalized to that of Rplp0 mRNA. Computed tomography was performed with the use of a Latheta LCT-100 machine (ALOKA). The adult male mouse brain at 9 weeks of age was scanned at 0.5-mm intervals. Brain volume was measured with the use of Latheta software (version 3.00, ALOKA). A gastrointestinal transit test was performed essentially as described previously34. Adult male mice at 9 weeks of age were deprived of food for 12 h with free access to water, after which 200 μl of a charcoal marker (10% charcoal, 5% gum arabic) was administered by oral gavage. Mice were killed 30 min later, and the intestine from the region of the pyloric sphincter to the ileo-caecal junction was removed for measurement (without stretching) of the length of the intestine and distance travelled by the marker. Chd8 mutant or wild-type mice were group-housed (three or four animals per cage) in a room with a 12-h-light, 12-h-dark cycle (lights on at 7:00) and with access to food and water ad libitum. Behavioural tests were performed with male mice at 12–50 weeks of age between 9:00 and 18:00 as described previously35, 36, 37, unless indicated otherwise. Each apparatus was cleaned with dilute sodium hypochlorite solution before testing of each animal to prevent bias due to olfactory cues. A wire-hang test apparatus (O’Hara & Co.) was used to assess balance and grip strength. The apparatus consists of a box (21.5 × 22 × 23 cm) with a wire-mesh grid (10 × 10 cm) on top that can be inverted. The male mice at 12–50 weeks of age were placed on the wire mesh, which was then inverted, causing the animal to grip the wire. The latency to the mouse falling was recorded, with a 60-s cutoff time. Motor coordination and balance were tested with the rotarod test. The mouse was placed on a rotating drum with a diameter of 3 cm (Accelerating Rotarod; UGO Basile), and the time that each animal was able to maintain its balance on the rod during its acceleration from 4 to 40 r.p.m. over 5 min was measured. The hot-plate test was used to evaluate sensitivity to a painful stimulus. The male mice at 13–16 weeks of age were placed on a hot plate at 55.0° ± 0.3 °C (Columbus Instruments), and the latency to the first hind-paw response (foot shake or paw lick) was recorded, with a cutoff time of 15 s. Each male mouse at 12–16 weeks of age was placed in the corner of an open-field apparatus (40 × 40 × 30 cm; Accuscan Instruments), which was illuminated at 100 lx. Total distance travelled, vertical activity (rearing, measured by counting the number of photobeam interruptions), and time spent in the central area (20 × 20 cm) were recorded over 120 min. The apparatus for the light-dark transition test consisted of a cage (21 × 42 × 25 cm) that was divided into two sections of equal size by a partition with a door (O’Hara & Co.). One chamber was made of white plastic and brightly illuminated, whereas the other was black and dark. The male mice at 12–15 weeks of age were placed in the dark side and allowed to move freely between the two chambers with the door open for 10 min. The number of transitions between the two compartments, latency to first entry into the light chamber, distance travelled, and time spent in each chamber were recorded with the use of ImageLD software (see ‘Data analysis’ below). The apparatus consisted of two open arms (25 × 5 cm) and two enclosed arms of the same size with 15-cm-high transparent walls (O’Hara & Co.). The arms and central square were made of white plastic plates and were elevated to a height of 55 cm above the floor. The likelihood of animals falling from the apparatus was minimized by attachment of 3-mm-high plastic ledges to the open arms. Arms of the same type were arranged on opposite sides. Each male mouse at 13–16 weeks of age was placed in the central square of the maze (5 × 5 cm) facing one of the closed arms, and its behaviour was recorded over 10 min. The number of entries into and the time spent in the open and enclosed arms were measured with the use of ImageEP software (see ‘Data analysis’). A startle-reflex measurement system (O’Hara & Co.) was used to measure startle response and prepulse inhibition (PPI). Each male mouse at 16–19 weeks of age was placed in a Plexiglas cylinder and left undisturbed for 10 min. White noise (40 ms) was used as the startle stimulus for all trial types. The startle response was recorded for 400 ms (with measurement of the response every 1 ms) starting with the onset of the startle stimulus. The background noise level in the chamber was 70 dB. The peak startle amplitude recorded during the 140-ms sampling window was measured. A test session consisted of six trial types (two types for startle stimulus–only trials and four types for PPI trials). The intensity of the startle stimulus was 110 or 120 dB. The prepulse sound (74 or 78 dB) was presented 100 ms before the startle stimulus. Four combinations of prepulse and startle stimuli (74 and 110, 78 and 110, 74 and 120, and 78 and 120 dB) were applied. Six blocks of the six trial types were presented in pseudorandom order such that each trial type was presented once within a block. The average intertrial interval was 15 s (range, 10 to 20 s). The forced-alternation task was performed with an automatic T-maze38 constructed of white plastic runways with walls 25 cm in height. The maze is partitioned into six areas by sliding doors that open downward. The stem of the T comprises area S2 (13 × 24 cm), and the arms of the T comprise areas A1 and A2 (11.5 × 20.5 cm). Areas P1 and P2 are connecting passageways from each arm (A1 or A2) to the starting compartment (area S1). The end of each arm is equipped with a pellet dispenser for provision of a food reward. Pellet sensors automatically record pellet intake by the mouse. One week before pretraining, the male mice at 42–46 weeks of age were deprived of food until its body weight was reduced to 80 to 85% of the initial value. It was then fed a maintenance diet throughout the course of all T-maze experiments. Before the first trial, the animal was subjected to a 30-min habituation session, during which it was allowed to freely explore the T-maze with all doors open and both arms baited with food. Beginning 1 day after habituation, the animal was subjected to daily pretraining. With all the doors closed and a pellet deposited in the food tray, the mouse was placed in area A1. After it had consumed the pellet or after 5 min had elapsed without pellet consumption, the mouse was transferred to area A2 and the process was repeated. Such pretraining was repeated five times a day and continued until the animal consumed more than 80% of the pellets provided during a day. Beginning on the day after completion of pretraining, the mouse was subjected to a forced-alternation protocol for 8 days (one session consisting of 10 pairs of training trials per day, with a cutoff time of 50 min). For the first (sample) trial of each pair, the mouse was forced to choose one of the arms of the T (A1 or A2) and received a reward at the end of the arm. After the mouse had consumed the pellet or it had stayed for >10 s without consuming the pellet, the door separating the arm (A1 or A2) and connecting passageway (P1 or P2) was opened to allow the mouse to return to the starting compartment (S1). The mouse was then given a 3-s delay followed by a free choice between the two T arms and was rewarded for choosing the arm that was not selected for the first trial of the pair. Choosing the incorrect arm resulted in no reward and confinement to the arm for 10 s. The location of the sample arm (left or right) was varied in a pseudorandom manner across trials with the use of a Gellermann schedule so that each mouse received equal numbers of left and right presentations. Various fixed extramaze cues surrounded the apparatus. On days 6–8, a delay (3, 10, 30 or 60 s) was applied between the forced- and free-choice trials of each pair. Data acquisition and analysis were performed automatically with ImageTM software (see ‘Data analysis’). The left–right discrimination task was performed with an automatic T-maze38 and after food deprivation as described above for the forced-alternation task. On the day after completion of the forced-alternation task, male mice at 44–48 weeks of age were subjected to the left-right discrimination task for 16 days (one session consisting of 10 trials, two sessions per day, with a cutoff time of 50 min). The animal was able to freely choose either the right or left arm of the T-maze (A1 or A2), with the correct arm being randomly assigned to each mouse. If it chose the correct arm, it received a reward at the end of the arm. Selection of the incorrect arm resulted in no reward and confinement to the arm for 10 s. After the mouse had consumed the pellet or stayed in the arm for >30 s without consuming the pellet, the door that separated the arm (A1 or A2) and connecting passageway (P1 or P2) was opened to allow the mouse to return to the starting compartment (S1). On the day 12, the correct arm was changed for reversal learning. A variety of fixed extramaze clues surrounded the apparatus. Data acquisition and analysis were performed automatically with ImageTM software (see ‘Data analysis’). In the social-interaction test, two male mice at 13–17 weeks of age of identical genotypes that were previously housed in different cages were placed together in a box (40 × 40 × 30 cm) and allowed to explore freely for 10 min. Analysis was performed automatically with the use of ImageSI software (see ‘Data analysis’). The total number of contacts, total contact duration, mean duration per contact, and total duration of active contacts were measured. Images were captured at a rate of three frames per second, and the distance travelled between two successive frames was determined for each mouse. If the two mice contacted each other and the distance travelled by either mouse was >10 cm, then the behaviour was considered an active contact. Active contacts included sniffing and following behaviour. The testing apparatus consisted of a rectangular, three-chambered box with a lid fitted with an infrared video camera (O’Hara & Co.). Each chamber measured 20 × 40 × 22 cm, and the dividing walls were made from clear Plexiglas, with small rectangular openings (5 × 3 cm) allowing access into each chamber. An unfamiliar male mouse (stranger 1) that had had no prior contact with the subject mouse was placed in one of the side chambers. The location of stranger 1 in the left versus right side chamber was systematically alternated between trials. The stranger mouse was enclosed in a small, round wire cage that allowed nose contact between the bars but prevented fighting. The cage was 11 cm in height, with a bottom diameter of 9 cm and vertical bars 0.5 cm apart. The subject mouse was first placed in the middle chamber and allowed to explore the entire test box for 10 min. The amount of time spent around the cage was measured with the aid of the camera fitted on top of the box in order to quantify social preference for stranger 1. A second unfamiliar male mouse (stranger 2) enclosed in an identical small wire cage was then placed in the chamber that had been empty during the first session. The test mouse thus had a choice between the first, already-investigated unfamiliar mouse (stranger 1) and the novel unfamiliar mouse (stranger 2). The amount of time spent around each cage during a second 10-min session was measured as before. All the mice used in this test were at 14–19 weeks of age. Data acquisition and analysis were performed automatically with the use of ImageCSI software (see ‘Data analysis’). This test was conducted in a manner similar to that for the sociability and social-novelty preference test. An unfamiliar male mouse (stranger) that had had no prior contact with the subject mouse as well as a cagemate of the subject mouse were placed in the side chambers. The test mouse thus had a choice between an unfamiliar mouse (stranger) and a familiar mouse (cagemate). All the mice used in this test were at 47–50 weeks of age. The Barnes maze test was performed on ‘dry land’, a white circular surface, 1.0 m in diameter, with 12 holes equally spaced around the perimeter (O’Hara & Co.). The circular open field was elevated 75 cm from the floor. A black Plexiglas escape box (17 × 13 × 7 cm) containing paper cage-bedding on its floor was located under one of the holes. The hole above the escape box represented the target, analogous to the hidden platform in the Morris task. The location of the target was consistent for a given mouse but was randomized across mice. The maze was rotated daily, with the spatial location of the target unchanged with respect to visual room cues, to prevent bias based on olfactory or proximal cues within the maze. Three trials per day were conducted. A probe trial was performed without the escape box at 24 h after the last training session to confirm that this spatial task was dependent on navigation based on distal environmental cues in the room. The location of the target for each mouse was then shifted to the opposite side of the circular surface, and the same protocol for training and probe trials was followed. All the mice used in this test were male at 17–21 weeks of age. Latency to reach the target hole, number of errors, and the time spent around each hole were recorded with the use of ImageBM software (see ‘Data analysis’). The grooming test was performed as previously described16. Each male mouse at 14–17 weeks of age was placed individually into a new standard cage. After a 10-min habituation period, the animal was videotaped for a 10-min test period and the time spent engaged in grooming behaviour (paw licking, body grooming or scratching, and head, hind leg or genital washing) was determined. Each male mouse at 17–20 weeks of age was housed individually in cages containing paper-chip bedding and one square of pressed cotton (Nestlet; Ancare). No other nesting material was present. After 1 h, manipulation of the Nestlet and the constitution of the built nest were assessed according to a five-point scale as described previously39: (1) Nestlet not noticeably touched; (2) Nestlet partially torn; (3) Nestlet mostly shredded but with no identifiable nest site; (4) an identifiable but flat nest; and (5) a (near) perfect nest. The apparatus consisted of four Plexiglas cylinders (20 cm in height and 10 cm in diameter) that were filled with dilute sodium hypochlorite solution at 23 °C up to a height of 7.5 cm. Each male mouse at 16–20 weeks of age was placed in the cylinders, and immobility time was recorded over a 10-min test period. Images were captured at a rate of two frames per second. For each pair of successive frames, the area (number of pixels) within which the mouse moved was measured. When this area was below a certain threshold, the mouse was judged to be immobile. When the area equalled or exceeded the threshold, the mouse was considered to be moving. The optimal threshold was determined by adjustment based on the degree of immobility measured by human observation. Immobility lasting <2 s was not included in the analysis. Data acquisition and analysis were performed automatically with the use of an ImageTS/PS software (see ‘Data analysis’). ChIP was performed essentially as described previously40. MEFs or nuclear extracts of male mouse brain at 13 weeks of age were fixed with a final concentration of 0.5% paraformaldehyde, suspended in ChIP buffer (5 mM HEPES-KOH (pH 8.0), 200 mM KCl, 1 mM CaCl , 1.5 mM MgCl , 5% sucrose, 0.5% Nonidet P-40, aprotinin (10 μg ml−1), leupeptin (10 μg ml−1), 1 mM phenylmethylsulfonyl fluoride), incubated for 10 min on ice, subjected to ultrasonic treatment with the use of a Diagenode Bioruptor, and digested with micrococcal nuclease (New England Biolabs) for 40 min at 30 °C. After the addition of EDTA to a final concentration of 0.1 mM, the digested sample was centrifuged at 15,000g for 10 min at 4 °C, and the resulting supernatant was incubated with rotation overnight at 4 °C with antibodies conjugated to magnetic beads. Bound proteins were eluted from the beads, and cross-links were reversed by incubation overnight at 65 °C with 1% SDS in Tris-EDTA buffer. After washing twice both with ChIP buffer and with Tris-EDTA buffer, DNA was purified with the use of a Qiaquick PCR Purification kit (Qiagen) and subjected to real-time PCR analysis as described above. PCR primers (sense and antisense, respectively) were as follows: Pten, 5′-GTGAGGGGGAGAGGTGTG-3′ and 5′-TGGATCGCACTAGCTGACC-3′; Nras, 5′-TGTATCACGGGAACGGATTGG-3′ and 5′-ACCCCTGAGCTGACCCTTGTC-3′; and Rac1, 5′-TCATACCGTCGTGAGGTTC-3′ and 5′-CCTGGTGGCTCACCTGTAATC-3′. ChIP–seq was performed essentially as described previously40. The whole brain was rapidly dissected from E14.5 or adult (13 weeks of age) male mice and homogenized with the use of a Potter homogenizer in a solution containing 10 mM HEPES-NaOH (pH 7.4), 1.5 mM MgCl , 10 mM KCl, 1 mM dithiothreitol, aprotinin (10 μg ml−1), leupeptin (10 μg ml−1), and 1 mM phenylmethylsulfonyl fluoride. The homogenate was centrifuged at 800g for 5 min at 4 °C, and the resulting pellet was suspended in homogenization buffer containing 0.6% Nonidet P-40, incubated for 10 min at 4 °C, and then centrifuged again at 800g for 5 min at 4 °C. The new pellet was subjected to ChIP with anti-CHD8 monoclonal antibody or antibodies to REST (17-641) as described above, and the precipitated and purified DNA was sequenced with a HiSeq 1500 system (Illumina). The reads were uniquely mapped to the mouse (mm9) genome with the use of Bowtie software (version 2.2.5), and duplicated reads were removed with samtools (version 0.1.19-44428cd). The mapped read counts were calculated for 200-bp intervals (bins) of the genome and then normalized as FPKM (fragments per kilobase of transcript per million reads)41. The ChIP–seq signal intensities were calculated as the FPKM difference between ChIP and input DNA for each bin. Markedly enriched regions of the genome were identified with the use of the MACS peak caller (version 2.1.0.20140616, with the option ‘-gsize mm-nomodel-extsize 160-broad-to-large-pvalue 1e-3’) and additional filtering based on a more stringent cutoff for the data from adult (P < 1 × 10−30) or E14.5 (P < 1 × 10−6) mouse brain. All RNA-seq and ChIP–seq analyses were performed with biological triplicates for each experimental time point in this study. CHD8 binding peaks were classified as active promoters, enhancers, or inactive regions according to the pattern of histone modification within the region spanning 2 kb upstream and 2 kb downstream of each peak. ChIP-seq data for histone modification in whole brain of E14.5 mouse were obtained from ENCODE at UCSC (https://genome.ucsc.edu/ENCODE). Total RNA was extracted from the whole brain of a male mouse at 13 weeks of age with the use of a TRIzol Plus RNA Purification Kit (Life Technologies). RNA-seq was performed as described previously40. Complementary DNA was sequenced with a HiSeq 1500 system (Illumina). The total amount of each transcript was calculated with the use of a series of programs including TopHat (version 2.0.10, with the option ‘-library-type fr-secondstrand’) and Cufflinks (version 2.1.1, with the option ‘-u -G-library-type fr-secondstrand’). RNA-seq reads were mapped against the mouse (mm9) genome. GSEA is a computational method that determines whether an a-priori-defined set of genes shows statistically significant, concordant differences between two biological states42. The primary result of GSEA is the enrichment score (ES), which reflects the degree to which a gene set is overrepresented at the top or bottom of a ranked list of genes. The top portion of a GSEA plot (represented by a green line) shows the running ES for the gene set as the analysis moves down the ranked list. The score at the peak of the plot (the score farthest from 0) is the ES for the gene set. The normalized enrichment score (NES) represents this ES value normalized by mean ES for all permutations of the data set and takes into account differences in gene set size. The NES value represents the degree of expression skew. The bottom portion of the plot shows where the members of the gene set (indicated by black bars) appear in the ranked list of genes. If most of the black bars are gathered to the left side (or right side), then most of the genes in the gene set are upregulated in the sample named at the lower left (or lower right). GSEA was performed as described previously42 with the use of GSEA v.2.0.1 (http://www.broadinstitute.org/gsea). The gene set collections H (Hallmark gene sets; 50 gene sets), C2 (curated; 4,725 gene sets), and C3.tft (motif gene sets, transcription factor targets; 615 gene sets) were obtained from Molecular Signature Database (MSigDB version 5.0; Broad Institute, http://www.broadinstitute.org/gsea/msigdb). H gene sets are recommended by the developer of GSEA for initial analysis as a starting point, and they include gene collections sorted according to biological states and processes. To identify gene sets affected by CHD8 haploinsufficiency in a comprehensive manner, we examined C2 gene sets, which comprise collections of genes represented in online pathway databases and publications in the PubMed database. The C3.tft gene sets comprise collections of genes harbouring the same cis-regulatory motif, and we used these sets to identify targets of transcription factors that are affected by CHD8 haploinsufficiency. Gene sets for differentially expressed genes in the frontal cortex (GSE28521)22 between ASD patients (aged 5–51 years, 12 males and 4 females) and healthy subjects (aged 16–56 years, 15 males and 1 female) were obtained from the bioinformatics platform NextBio. Neural development–related gene sets (early-fetal genes and mid-fetal genes) were defined by comparison of gene expression patterns in the human brain between developmental periods 2–3 (early-fetal) and 4–6 (mid-fetal)25. Human genes were converted to mouse orthologues with the use of BioMart (http://www.biomart.org). The 500 most upregulated genes at early-fetal and mid-fetal stages were selected for analysis. To compare with the significant gene sets (‘ASD-related gene set’ and ‘V$NRSF_01 gene set’) by GSEA, random gene sets were created from all annotated genes using ‘runif’ function, which generates random numbers from the uniform distribution, in the ‘stats’ package of R language (http://www.r-project.org/). The same number of genes in each significant gene set was randomly selected without duplication, and this procedure was independently iterated 1,000 times. The applications for behavioural studies (ImageLD, ImageEP, ImageTM, ImageSI, ImageCSI, ImageBM, ImageTS) were developed by T.M. and are based on ImageJ (http://rsb.info.nih.gov/ij/). Statistical analysis according to Student’s t-test, or two-way repeated-measures ANOVA was performed with the use of StatView 5.0.1 software (SAS Institute), and Wilcoxon rank-sum test, hypergeometric test, or Welch’s t-test was performed with the use of R language. Two-way ANOVA was applied to comparisons in behavioural tests, and individual P values for strain, genotype (or cage side), and their interaction (S × G or S × C)43 are presented. To control for type I errors due to multiple-hypothesis testing, we calculated the FDR by the Benjamini–Hochberg method44.


Patent
NextBio | Date: 2015-08-17

The present invention relates to methods, systems and apparatus for capturing, integrating, organizing, navigating and querying large-scale data from high-throughput biological and chemical assay platforms. It provides a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure. According to various embodiments, methods, systems and interfaces for associating experimental data, features and groups of data related by structure and/or function with chemical, medical and/or biological terms in an ontology or taxonomy are provided. According to various embodiments, methods, systems and interfaces for filtering data by data source information are provided, allowing dynamic navigation through large amounts of data to find the most relevant results for a particular query.


Patent
NextBio | Date: 2012-09-17

Provided herein are methods, systems and apparatus for querying and interpreting data derived from individual patients. The methods, systems and apparatus described herein can be used in clinical and research settings. Included are methods, systems and apparatus for identifying similar patients, germline DNA analysis, somatic tissue analysis, pathway-based therapy selection, prioritizing drugs, and querying a database to return patients and clinical attributes.


The present invention relates to methods, systems and apparatus for capturing, integrating, organizing, navigating and querying large-scale data from high-throughput biological and chemical assay platforms. It provides a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure.


Patent
NextBio | Date: 2010-06-08

According to various embodiments, aspects of the invention provide a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from diverse sequencing technologies as well as different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure. The methods, systems and apparatuses described enable combining orthogonal types of data and available public knowledge to elucidate mechanisms governing normal development, disease progression, as well as susceptibility of individuals to disease or response to drug treatments.


Patent
NextBio | Date: 2010-06-08

According to various embodiments, aspects of the invention provide a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from diverse sequencing technologies as well as different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure. The methods, systems and apparatuses described enable combining orthogonal types of data and available public knowledge to elucidate mechanisms governing normal development, disease progression, as well as susceptibility of individuals to disease or response to drug treatments.


Patent
NextBio | Date: 2015-09-21

According to various embodiments, aspects of the invention provide a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from diverse sequencing technologies as well as different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure. The methods, systems and apparatuses described enable combining orthogonal types of data and available public knowledge to elucidate mechanisms governing normal development, disease progression, as well as susceptibility of individuals to disease or response to drug treatments.


SAN FRANCISCO--(BUSINESS WIRE)--Illumina, Inc. (NASDAQ:ILMN) today announced Illumina Accelerator—the world’s first business accelerator focused solely on creating an innovation ecosystem for the genomics industry—has selected three new startups for its second funding cycle. Selected from a competitive pool of highly qualified applicants, the new startups from across the globe are spurring genomics innovation in healthcare, agriculture, and the winemaking industry. “We are delighted to invite such a promising group of cutting-edge startups to Illumina Accelerator,” said Illumina Senior Vice President and Chief Technology Officer, Mostafa Ronaghi, Ph.D. “Their ability to build innovative solutions to shape the future of genomics inspires us, and we’re thrilled to provide them with tools and resources to build a successful future.” The selected startups for the spring 2015 funding cycle are: Each startup will receive seed investment, a subscription to Illumina’s NextBio translational genomics database, access to match funding through the $40 million Illumina Accelerator Boost Capital, and Illumina’s sequencing systems and reagents. In addition, startups accepted into Illumina Accelerator will gain access to business guidance and fully operational lab space in the San Francisco Bay Area during the six-month funding cycle. “We look forward to building upon the successes of our first graduates—Encoded Genomics, Inc., EpiBiome, Inc., and Xcell Biosciences, Inc.—by helping our second group of startups also create significant value, generate terabases of sequencing data, and advance their genomics applications,” said Amanda Cashin, Ph.D., who leads Illumina Accelerator. Illumina Accelerator strives to catalyze genomics innovation in the broader startup community. It aims to advance genomics by lowering the barrier to entry and expedite the time to market for entrepreneurs and early-stage companies that are working on scientifically and commercially promising next-generation sequencing applications. Startups are invited to join Illumina Accelerator twice a year. Applications for the Illumina Accelerator Fall 2015 funding cycle are due by September 1, 2015. To apply, visit www.illumina.com/accelerator. Illumina is improving human health by unlocking the power of the genome. Our focus on innovation has established us as the global leader in DNA sequencing and array-based technologies, serving customers in the research, clinical, and applied markets. Our products are used for applications in the life sciences, oncology, reproductive health, agriculture, and other emerging segments. To learn more, visit www.illumina.com and follow @illumina. This release may contain forward-looking statements that involve risks and uncertainties. Important factors that could cause actual results to differ materially from those in any forward-looking statements are detailed in our filings with the Securities and Exchange Commission, including our most recent filings on Forms 10-K and 10-Q, or in information disclosed in public conference calls, the date and time of which are released beforehand. We do not intend to update any forward-looking statements after the date of this release.

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